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Career Crush: What Is It Like to Be a Software Engineer?

• Kelsey Alpaio

And how do you become one?

Where your work meets your life. See more from Ascend here .

I am fascinated by coding. It’s everywhere! Every single one of the digital experiences we enjoy is the result of code.

• KA Kelsey Alpaio is an Associate Editor at Harvard Business Review. kelseyalpaio

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College Essay Tips for Software Engineering Programs

This article was written based on the information and opinions presented by Hale Jaeger in a CollegeVine livestream. You can watch the full livestream for more info.

What’s Covered:

“why this . . .” essays for software engineering, writing your essay.

For many college applications, you’ll write essays in addition to the Common App personal statement . These prompts will often ask you about what you’re planning on pursuing at the college. This article will give you practical advice for explaining your interest in software engineering. 

Many supplemental essay prompts are quite common, such as “ Why this major? ” and “ Why this school? ” If you’re sure about pursuing software engineering and know which college you want to kick off your career at, you should already know the answers to these questions. 

Certain schools have strong software engineering and computer science programs. If this is the case for your chosen college, it should be easy for you to say that you can identify with their program. You can add that you’re excited to use the specific resources there and how they will help you reach your goal of becoming a software engineer.

When talking about your major, bring up what attracts you to the field. Your eventual salary and career prospects are incentives, but you want to explain what specifically about the study of computer science and engineering makes you excited. Why do you like to learn about it? Maybe you’re fascinated by the inner workings of technology. Perhaps you’re interested in how specific tools on certain websites work. It’s also possible that you want to improve user experience and innovate existing software.

These reasons are a bit less shallow than money. They also get to the heart of why you want to pursue software engineering: you like to build things and solve problems. 

From Abstract to Specific

In general, when writing your essays, you should work on funneling these types of ideas about your major from the abstract to the specific. You can open with a particular anecdote or story to catch the reader’s attention, of course, but try to start with high-level interests. Fundamental things like identifying the inner workings of a website can lead to more niche topics.

Personal Experiences

When writing your essays, make sure you touch on any personal experiences that can help show why this subject is your passion. It can all add to the personal narrative that you’ve been building in your entire application and help make the admissions officers understand you better.

If you had an experience with technology that fascinated you, drew you into the subject, and made you want to learn more, then include that. Be sure to add the important details so the reader can get a good sense of the scene. Another way to go is if you had the opposite experience: you encountered a frustrating piece of technology and were desperate to figure out how to get it working. You realized that you wanted to go into the field to improve software and make people’s lives easier. You can try writing about your interests that way. 

Another way to write your essay is to back up an explanation of your passions with a personal story that will make your essay compelling. Try to draw on an anecdote, and if possible, explain what you’ve accomplished after your initial interest was sparked. 

How did you get involved in coding? If you found technology that was glitching all the time or something that excited you, did this inspire you to figure out how it all worked? Write about how you’ve developed your skills in coding and science and how much you’ve learned about good systems and malfunctioning systems. Then, write about what you want to accomplish and innovate in the field.

Plans for the Future

When you’ve discussed the past and present, you can begin to probe the future. For the sake of narrative, try to include how you’ve grown and what your ultimate ambitions are. If you’re not sure exactly what branch of software engineering you want to go into, that’s fine. You can name a few options, such as game design or mobile design, or you can just talk about how you want to build things and make better technology to improve people’s lives. 

When you’re talking about personal things, you should aim to be specific. Draw on stories when you can, and be honest about what interests you about this subject and what you want to do in the field. This is your chance to explore why you’re looking to go into software engineering, so you should come away from these essays feeling much more confident about your planned course of study.

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Essay on Software Engineering

Students are often asked to write an essay on Software Engineering in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Software Engineering

Introduction to software engineering.

Software Engineering is a branch of computer science that deals with the design, development, and maintenance of software systems. It combines principles of engineering, computing, project management, and software design.

Role of a Software Engineer

Software engineers are responsible for creating software applications. They analyze user needs, design software solutions, test the software, and fix any bugs or issues that arise.

Importance of Software Engineering

Software Engineering is vital in today’s digital world. It helps in creating efficient and reliable software, ensuring that technology runs smoothly and meets user needs.

Software Engineering is a fascinating and vital field. It plays a crucial role in shaping our digital world and improving our lives.

250 Words Essay on Software Engineering

Software Engineering is a branch of computer science that involves the development and building of computer systems software and applications software. It integrates various principles and methodologies to design, develop, test, and maintain software solutions.

Principles and Methodologies

Software Engineering employs a systematic, disciplined and quantifiable approach to the development, operation, and maintenance of software. It encompasses methodologies like Agile, Waterfall, and Scrum, which provide a structured framework for software development.

Software Development Life Cycle (SDLC)

At the heart of Software Engineering is the Software Development Life Cycle (SDLC), which comprises several phases such as requirement gathering, design, coding, testing, deployment, and maintenance. Each phase has its own significance, and skipping any phase can lead to project failure.

Significance of Software Engineering

Software Engineering is critical in today’s digital age as it contributes to efficient and reliable software production. It ensures the development of high-quality software within budget and timelines, meeting both market and customer demands.

Emerging Trends in Software Engineering

The field is continually evolving with emerging trends such as Artificial Intelligence, Machine Learning, Blockchain, and DevOps, which are reshaping the software industry. These advancements are pushing the boundaries of Software Engineering, making it an exciting field to explore.

In conclusion, Software Engineering is a vital discipline that combines creativity, problem-solving, and technical skills. It is at the forefront of creating innovative solutions that transform the way we live and work.

500 Words Essay on Software Engineering

Software Engineering is a discipline that integrates the principles of computer science, mathematics, and engineering to design, develop, and maintain reliable and efficient software systems. It’s a vital field in our digital era, where software systems are integral to various aspects of human life, including healthcare, transportation, entertainment, and education.

The Core of Software Engineering

At the heart of software engineering lies the software development life cycle (SDLC), a structured process that includes stages such as requirements gathering, design, coding, testing, deployment, and maintenance. The SDLC is designed to ensure the delivery of high-quality software that meets user requirements and is maintainable, efficient, and reliable.

Software engineers also use design principles and patterns to create software systems that are robust, scalable, and easy to maintain. These principles guide the structuring of software components and their interactions, leading to systems that are easier to understand, modify, and extend.

Software Engineering Methodologies

Different methodologies guide the process of software development. Traditional methodologies, such as the Waterfall model, emphasize a sequential approach where each stage of the SDLC is completed before the next begins. In contrast, Agile methodologies, like Scrum and Kanban, promote flexibility, iterative development, and continuous customer feedback.

Quality Assurance in Software Engineering

Quality assurance is a critical aspect of software engineering. It involves a set of activities, including testing and code reviews, designed to ensure that the software meets specified requirements and is free from defects. Automated testing tools, continuous integration, and continuous deployment are commonly used practices in modern software development to ensure rapid feedback and high software quality.

The Role of Ethics in Software Engineering

Ethics in software engineering is a significant yet often overlooked aspect. Software engineers have a responsibility to ensure that the software they develop is not only functional and efficient but also respects user privacy, security, and societal norms. They must consider potential misuse of the software and strive to prevent it.

Future Trends in Software Engineering

As technology evolves, so does software engineering. Trends such as Artificial Intelligence, Cloud Computing, and DevOps are shaping the future of software development. Artificial Intelligence is being used to automate parts of the software development process, while Cloud Computing provides a scalable and cost-effective platform for deploying software applications. DevOps, a practice that emphasizes collaboration between development and operations teams, is becoming increasingly popular for its ability to deliver software faster and with fewer errors.

In conclusion, software engineering is a dynamic and evolving discipline that plays a crucial role in the digital world. It combines rigorous processes, methodologies, and principles with creativity and problem-solving skills to build software systems that power our world. As we move towards an increasingly digital future, the importance and relevance of software engineering will only continue to grow.

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Software Development is defined as the process of designing, creating, testing, and maintaining computer programs and applications. Software development plays an important role in our daily lives. It empowers smartphone apps and supports businesses worldwide.

According to the U.S. Bure­au of Labor Statistics, there is a projecte­d 21% increase in software de­veloper employment from 2018 to 2028, which is significantly higher than the national average­.

The demand for application deve­lopers is expected to grow by an impressive 26%, surpassing the me­re 5% average change­ in overall employment. This significant growth can be related to the rapid technological advances experienced over the last two decades.

Table of Content

Types of Softwares

Steps of Software Development

Features of Software Development

Why is software development important, jobs that require software development, faqs on software development.

Software de­velopment is defined as the process of designing, cre­ating, testing, and maintaining computer programs and applications. This diverse field combines creativity, engineering expertise, and problem-solving abilities to produce software that satisfies particular requirements and goals. Software developers, also known as programmers or coders, use a variety of programming languages and tools to create solutions for end-users or businesses.

Note : If you want to learn about Product Development, Please refer this: Product Development | Definition, Principles, Steps, Stages and Frameworks

Software developers develop the software, which itself is a set of instructions in order to perform a specific task. software have three types.

There are three basic types of Software

1. System Software

System software is software that directly operates computer hardware and provides basic functionality to users as well as other software for it to run smoothly.

2. Application Software

Application software is a software that is designed for end-user to complete a specific task. It is a product or programm that is only intended to meet the needs of end users. It includes word processors, spreadsheets, database management, inventory, and payroll software, among other things.

3. Programming Software

Programming software is a software that is designed for programmers to develop program. It consist of code editor, compiler, interpreter, debugger etc.

Under Software Development, developers develop all the software that comes under these three category.

Software de­velopment is a well-structured process with several key stages. While different methodologies exist, such as Agile and Waterfall, most software development projects include the following steps:

1. Requirement Analysis :

• The first step in software development is understanding the requirements and based on that requirement gathering happen. This stage involves identifying the needs, objectives, and constraints of the project. The goal is to define what the software should do and what problems it will solve.
• In the design phase, the software’s architecture and user interface are developed. This step defines how the software will work and how users will interact with it. Design includes creating wireframes, prototypes, and system architecture diagrams.
• After comple­ting the architectural design phase­, developers move­ on to creating detailed de­signs for each component of the syste­m. This includes designing not only the use­r interface but also encompassing database­s and APIs. The intricate decisions made­ in these detaile­d designs provide valuable guidance­ throughout the coding phase.

3. Implementation

• The most important phase of the Software Development is the implementation phase, which comes after the design phase. This phase will see the implementation of the design phase’s output.
• All of the planning done in the planning phase and the designing done in the designing phase are implemented in this phase. Physical source code is created and deployed in the real world during this phase.

4. Testing:

• De­velopers utilize unit te­sts to evaluate small code compone­nts, such as functions or methods. These te­sts play a crucial role in identifying and resolving bugs within isolate­d elements.
• Integration testing evaluate­s the smooth functioning of various software components. Its purpose­ is to ensure seamle­ss interactions betwee­n modules and efficient data transfe­r among them, resulting in a robust system.
• In order to ensure that the­ software meets all the­ specified require­ments, system testing e­valuates it as a whole. This comprehe­nsive evaluation includes functional, pe­rformance, security, and other ne­cessary types of testing.
• User Acce­ptance Testing (UAT) occurs during the phase­ where end-use­rs or clients validate the software­ to ensure it mee­ts their requireme­nts. Identified issues or discre­pancies are promptly addresse­d before procee­ding with deployment.

5. Deployment:

• Before deployment, the development team configures the target environment, whether it’s on-premises servers, cloud-based infrastructure, or end-user devices. This may involve setting up servers, databases, and configuring software dependencies.
• Developers carefully plan the process of deploying software, which includes aspects such as data migration strategies, software installation procedures, and contingency measures for unexpected issues.
• The software­ is deployed to end-use­rs or production environments. Ongoing monitoring is critical for quickly identifying and addressing any issues that may arise following the deployment.

6. Maintenance and Updates:

• Once­ the software has bee­n deployed, it is common for issues and bugs to arise­. The dedicated te­am of developers active­ly works on identifying, fixing, and thoroughly testing these­ problems. Regular updates are­ provided to address any nece­ssary improvements or changes that may arise­
• Feature­ enhancements are­ made to the software as use­r needs evolve­ or new requireme­nts arise. Develope­rs consistently implement ne­w features and improveme­nts in response to these­ changes.
• Regular security updates are crucial to address vulnerabilities and protect the software from cyber threats.

7. Documentation:

• The software developer provides use­r guides, manuals, and online help docume­ntation to assist end-users effe­ctively navigate its feature­s.
• Deve­lopers are responsible­ for creating technical documentation that outline­s the architecture, code­ structure, and APIs of a system. This documentation is crucial in he­lping future develope­rs comprehend and maintain the software­.
• Collaborative Nature: Software development is a collaborative process that involves a diverse group of professionals, including developers, designers, project managers, and stakeholders. Software project success is heavily dependent on effective communication and seamless teamwork.
• Continuous Learning : In Software Development it’s super important to keep learning because things are always changing. New ways of writing code, tools, and technologies are always popping up. To do well and keep up, programmers need to keep on learning and getting better at what they do. It’s like an ongoing adventure of picking up new skills to stay on top of the game.
• Problem-Solving: Deve­lopers play a crucial role as problem solve­rs. They actively identify and addre­ss issues, craft innovative solutions, and optimize code­ to achieve the de­sired outcomes. Problem-solving skills lie­ at the heart of the software­ development proce­ss.
• Creativity: When Developers making computer programs, it’s not just about following rules . There’s also room for being creative. Coding needs a lot of attention to detail and clear thinking, but it’s also a chance to let developers imagination run wild.
• Quality Assurance : In development, ensuring the­ quality and reliability of the software is a crucial aspe­ct. To ensure exceptional results, the development cycle includes stringent testing and quality assurance procedures.

Software development is critical because it creates the computer program and apps that we use every day, allowing things to run more smoothly and making our lives easier. It’s like the hidden magic that makes technology work for us.

1. Enabling Technological Innovation

Software­ development plays a crucial role­ in technological advancements. Software develope­rs are responsible for creating innovative smartphone­ applications, designing we­bsites, or developing comple­x enterprise software.

2. Improved Efficie­ncy

In various industries, software deve­lopment plays a crucial role in automating tasks and processe­s. This automation leads to enhanced e­fficiency. Consider the busine­ss sector as an example. It utilize­s software applications to streamline ope­rations, effectively manage­ resources, and facilitate informe­d decision-making processes.

3. Adapting to Changing Nee­ds

Software developme­nt offers the nece­ssary flexibility and adaptability, allowing develope­rs to continually update and modify software in response­ to evolving user nee­ds, regulatory requireme­nts, and business demands. This ability to adapt holds paramount importance in e­ffectively navigating the rapid change­s of the digital domain.

4. Global Reach

The­ internet has revolutionize­d connectivity by bridging gaps across continents. With the aid of software­ applications, both businesses and individuals can effortle­ssly tap into a worldwide audience, shatte­ring geographical boundaries and unlocking boundless marke­t potential.

The field of software development offers a wide range of career opportunities, each with its own set of responsibilities and specializations. Some of the key roles in the software development industry include:

• Software Developer/Programmer: Software de­velopers, also known as programmers, have­ the important task of writing code and deve­loping applications to meet project re­quirements. They spe­cialize in various areas such as web de­velopment, mobile app de­velopment, or back-end syste­ms development. The­ir role involves ensuring that the­ software functions effective­ly and fulfills its intended purpose.
• Front-End Developer: In the fie­ld of web developme­nt, a Front-End Developer is re­sponsible for crafting the visual interface­ and enhancing user expe­rience on website­s and applications. Their expertise­ lies in utilizing HTML, CSS, and JavaScript to design and impleme­nt visually compelling eleme­nts within software.
• Back-End Developer: In the fie­ld of software­ development, the­re exists a crucial role known as the­ Back-End Developer. The­se talented individuals posse­ss expertise in se­rver-side programming, managing databases, and e­nsuring efficient serve­r functionality. It is their responsibility to construct the unde­rlying infrastructure
• DevOps Engineer : The De­vOps Engineer plays a crucial role in bridging the­ gap between de­velopment and IT operations. The­y facilitate a seamless proce­ss by automating deployment, testing, and monitoring of software­. Their responsibilities e­ncompass ensuring efficient de­velopment and deployme­nt procedures.
• Quality Assurance (QA) Engineer: The QA e­ngineer is responsible­ for testing and ensuring the quality and functionality of software­. They carefully design te­st cases, execute­ tests, and diligently report any de­fects to the deve­lopment team.
• Software Architect: The software­ architect is responsible for de­signing the overall structure and syste­m of a software project. They make­ important high-level design de­cisions and establish the project’s te­chnical direction.
• Product Manager : A Product Manage­r oversees the­ entire deve­lopment process, from gathering re­quirements to deployme­nt. They are responsible­ for defining project goals, prioritizing feature­s, and ensuring that the final product aligns with business obje­ctives.
• Data Scientist/Engineer: Data scientists and e­ngineers are e­xperts in the manipulation and analysis of data. Their focus lie­s in creating data-driven applications and algorithms that bene­fit both businesses and rese­arch endeavors.
• Cybersecurity Analyst: With the growing importance of cybersecurity, analysts in this field focus on securing software and systems against cyber threats and vulnerabilities.

Conclusion: Software Development

Software de­velopment is a broad field that constantly e­volves and shapes the mode­rn world. Its impact is far-reaching, from user-friendly mobile­ apps to intricate business systems. By following a structure­d process, fostering creativity, and e­mphasizing quality assurance, develope­rs drive the growth and adaptation of software solutions in our incre­asingly digital society. The diverse­ range of career opportunitie­s within this industry provides passionate individuals with a chance to make­ a significant impact on the future of innovation and technology.

1. What is meant by software developer ?

• Software developers develop the software and are responsible for the activities related to software, which include designing, programming, creating, implementing, testing, deploying, and maintaining software.

2. What is the full form of SDLC ?

• SDLC stands for Software Development Life Cycle.

3. Is software development the same as coding?

• Coding is a part of software development, apart from that software development consist of other things like planning, designing, developing, testing, deployment and maintenance. In software Development, with the help of coding developers give instruction to computer about how to perform specific task for a program.

4. What Does a Software Developer Do?

• A software developer creates computer programs or applications. They use their coding skills to write instructions that tell computers what to do. They develop instructions that tell computers what to do using their coding talents. It’s similar to providing step-by-step instructions for creating software that can solve problems, play games, or assist with other activities.

5. What are some software development projects?

Some of the major software development projects are :

• E – commerce Website
• Library Management System
• E portfolio Website

Check out some software development projects using this link !!

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How writing can advance your career as a developer

“In their first few years on the job, engineers spend roughly 30% of their workday writing, while engineers in middle management write for 50% to 70% of their day; those in senior management reportedly spend over 70% and as much as 95% of their day writing.” - Jon Leydens

I didn’t take a single English class to receive my undergraduate engineering degree. It’s a shame because writing has been arguably one of the most important skills I’ve had in my career as a software engineer and team leader.

I got my second internship in college thanks to a strong cover letter. As a new graduate, I got my first job by sending a cold email to an interesting startup I found online. When I was put in charge of an engineering team a few years later, two of my first few hires knew me through my blog before applying. And, in 2020, I left my role as a CTO to start a technical writing business because so many companies were asking me to write developer-focused content.

While my experience might be unique (very few engineers go on to become professional writers), writing is an important skill for all of us in software development. According to an IEEE article , engineers spend a large part of their day writing, and it only increases as they get more senior:

“In their first few years on the job, engineers spend roughly 30% of their workday writing, while engineers in middle management write for 50% to 70% of their day; those in senior management reportedly spend over 70% and as much as 95% of their day writing.” - Jon Leydens as cited in The Writing Engineer

The move towards remote work over the past year has also reminded many managers of how important it is for their teams to be able to write. 30% of respondents to Upwork’s Future of Work Survey cited communication issues as one of the biggest challenges in going remote.

Software engineering is a team sport

If you’re new to engineering, you might have the misconception that software development is largely done in quiet rooms full of developers independently writing code.

While writing code is part of the job, the other, often larger component is deciding what code to write and how to write it. This portion is largely collaborative as business, technical, and interpersonal interests must work in tandem to produce any significant piece of software.

“Every industry has truths that are obvious to those who have spent time working in the industry, but may be surprising to those on the outside. One such truth for software engineers: our jobs involve an awful lot of writing.” - Ben McCormick, Engineering Manager at Kustomer

Most production-ready software projects are built by large groups of people, and those people have to communicate. Whether you are creating technical documentation, giving another team member feedback on their pull request, planning a new project, or answering a question on Stack Overflow , it’s likely that you’ll spend at least a little time writing something every day as a software developer.

Writing ability might be a baseline requirement for many software development jobs, but it’s not a skill that developers typically think about improving for their careers. While it’s tempting to invest all your spare time learning new frameworks and languages, improving your writing might actually be a better way to advance your career and stand out in tech.

For this piece, I spoke to eight software developers to learn more about how writing has helped them advance their careers. I then distilled their stories into five specific benefits that writing has given them throughout their careers and added a bit of my own experience as well.

1. Writing reinforces learning

As software engineers, we have to constantly be learning new things. According to the most recent Stack Overflow developer survey , “75% of respondents noted that they learn a new technology at least every few months or once a year.”

Educators have understood the value of writing as a learning tool for years, and everyone I talked to mentioned that writing helped them reinforce new concepts too. If you write publicly, you get the dual advantage of possibly teaching other engineers some of the concepts you’ve learned.

“Writing code to solve a problem is one thing, but explaining that solution to a community of developers on the internet is another. You want to make sure you absolutely know what you're saying; which means research, lots of research!” - Daniel Phiri , Developer Relations at Strapi

Eze Sunday , a software developer and freelance writer, agreed, adding, “if you can't teach it, then you don't really know it.”

I’ve never been a note-taker, but I’ve always tried to write blog posts about new things that I’m learning. Very few of these posts got a lot of readers, but they were a great way for me to reinforce new technology or tools that I had recently learned. Adam DuVander , a developer, consultant, and author of Developer Marketing Does Not Exist , gave me similar advice:

“Look back at your most recent commits. Pick a fun technical challenge you faced and share how you fixed it. If you do this every month or two, you’ll have more technical posts than almost any other working engineer.” - Adam DuVander

2. Writing can help you find jobs and clients

Writing can help reinforce topics that you know, but it’s also a window into your skills as a software engineer .

“[Writing] is social proof of my ability to learn in public,” Dan Moore , Head of Developer Relations at FusionAuth told me. “My writing was instrumental in getting my first job in developer relations, as I met the company at a conference and was able to show them work examples.”

“I owe my entire career to a couple of articles I wrote,” Adam DuVander told me. “A tutorial I wrote on Webmonkey led to my first developer job. They saw how I discussed the technology and knew before we even chatted that I could handle the work.” He went on to add that writing helped him get a job with ProgrammableWeb among other career opportunities. “My whole career really all comes back to writing,” he said.

Stephanie Morillo , a technical program manager and author of The Developer’s Guide to Content Creation , had several examples of how writing has helped her on her career journey:

“I once got a full-time offer to join a cloud computing startup as a copywriter on the strength of a few blog posts I'd written. I wrote a few articles about tech culture in the mid-2010s and was able to secure conference speaking engagements from them. I was offered a role as a part-time technical writer for an open-source organization, and I even started doing freelance copywriting on the side for [software development] agencies.” - Stephanie Morillo

John Gramila and Keanan Koppenhaver , both software consultants in Chicago, had similar stories of getting new clients thanks to articles they’ve published about various software engineering topics. “People want to engage and want to reach out,” Keanan said, “but if you never put yourself out there with something you've written, you won't see many of those opportunities.”

3. Writing can lead to book authorship and public speaking opportunities

Back in 2017, I challenged myself to write something every day. Most of the pieces that came out of that experiment were random programming topics I was learning, but for about three months, I focused on a series of articles about using PHP with Docker.

This led to a short, self-published book , conference speaking opportunities, and lots of consulting offers over the years. I didn’t feel like I was an expert on PHP or Docker, but because very few people were writing about the topics publicly, my work stood out.

Dan Moore had a similar experience, turning a collection of his blog posts into a full-length book ( Letters to a New Developer ). James Hickey , a Senior Software Engineer and Microsoft MVP, echoed similar opportunities thanks to his writing. “I have had many people reach out to me about doing contract work simply by reading my blog and had many offers to write books in the last couple of years.”

Adam DuVander pointed out that taking on projects like writing a book or speaking at a conference is a career differentiator as well. “Instead of competing with all other engineers, you become The Choice in your area,” he told me. “Write about it and if there’s enough business interest, you’ll find a great role.”

4. Preserves your personal historical record

If you work for a company with restrictive intellectual property rules, you might not be able to share much about your day job publicly, but even writing privately can be valuable.

Stephanie Morillo told me that she recommends developers try journaling. “Journaling gives you the opportunity to write without being self-conscious because you're not writing with an audience in mind; you're doing it for yourself.”

Dan Moore added that “Writing serves as a historical record, but more importantly it clarifies your thoughts. I often write down a question or issue I'm facing and find that I see new avenues for exploration.”

Recording your logic at a point in time is also important because it’s likely to change (and hopefully improve) over time. I’ve found myself coming back to the same ideas and engineering problems repeatedly over the years.

This revisitation of the same topics is now part of my writing process , as each time I write about something, my ideas and arguments get a little stronger. In the same way that Fred Brooks warns us that “In most projects, the first system built is barely usable,” I find the first piece I write about a topic is much less compelling than later iterations.

5. Writing opens up new career opportunities

Finally, having both writing and programming skills opens you up to entirely new career options. Whether you’re experiencing burnout or simply want to look for new challenges, developers who can write have a lot of options for alternative career paths .

“The options are almost infinite, but include product, technical account management, marketing, sales engineering, and more. You can combine your authentic technical background with the ability to communicate it in a role where those skills are both much needed and rare.” - Adam DuVander

While writing may not be quite as lucrative as software development, there are plenty of hybrid roles like technical writing, developer relations, and technical training that offer very good salaries and career advancement opportunities.

I don’t expect many developers to take these alternative pathways, but it’s helpful to know these roles exist. Many people who get burned out of software development have a hard time deciding what they can do with their skills, but if you enjoy writing, there are plenty of unconventional options.

Getting started

Writing is an essential part of modern software development, and it’s only getting more critical as remote work becomes increasingly common. That said, you don’t have to start a public blog just to get started. Taking on small projects like answering Stack Overflow questions, writing Twitter threads, keeping a journal, or taking extra time on your company’s internal documentation are all good ways to get started.

If you want to start your own blog, Medium , Dev.to , and Hashnode are all popular options for developers. Or, if you’d prefer to get paid to write, there are many great technical blogs that pay contributors .

However you do it, I’d encourage you to just get started. When you do, let me know about your journey on Twitter . I’d love to follow along!

Software development refers to a set of computer science activities that are dedicated to the process of creating, designing, deploying, and supporting software.

Software itself is the set of instructions or programs that tell a computer what to do. It is independent of hardware and makes computers programmable. There are three basic types:

System software to provide core functions such as operating systems, disk management, utilities, hardware management and other operational necessities.

Programming software to give programmers tools such as text editors, compilers, linkers, debuggers, and other tools to create code.

Application software (applications or apps) to help users perform tasks. Office productivity suites, data management software, media players and security programs are examples. Applications also refer to web and mobile applications like those used to shop on Amazon.com, socialize with Facebook or post pictures to Instagram. 1

A possible fourth type is embedded software . Embedded systems software is used to control machines and devices not typically considered computers — telecommunications networks, cars, industrial robots and more. These devices, and their software, can be connected as part of the Internet of Things (IoT). 2

Software development is primarily conducted by programmers, software engineers and software developers. These roles interact and overlap, and the dynamics between them vary greatly across development departments and communities.  

Programmers, or coders , write source code to program computers for specific tasks like merging databases, processing online orders, routing communications, conducting searches, or displaying text and graphics. Programmers typically interpret instructions from software developers and engineers and use programming languages like C++ or Java to carry them out.

Software engineers apply engineering principles to build software and systems to solve problems. They use modeling language and other tools to devise solutions that can often be applied to problems in a general way, as opposed to merely solving for a specific instance or client. Software engineering solutions adhere to the scientific method and must work in the real world, as with bridges or elevators. Their responsibility has grown as products have become increasingly intelligent with the addition of microprocessors, sensors, and software. Not only are more products relying on software for market differentiation, but their software development must be coordinated with the product’s mechanical and electrical development work.

Software developers have a less formal role than engineers and can be closely involved with specific project areas — including writing code. At the same time, they drive the overall software development lifecycle — including working across functional teams to transform requirements into features, manage development teams and processes, and conduct software testing and maintenance. 3

The work of software development isn’t confined to coders or development teams. Professionals such as scientists, device fabricators and hardware makers also create software code even though they are not primarily software developers. Nor is it confined to traditional information technology industries such as software or semiconductor businesses. In fact, according to the Brookings Institute (link resides outside ibm.com), those businesses “account for less than half of the companies performing software development.”

An important distinction is custom software development as opposed to commercial software development. Custom software development is the process of designing, creating, deploying, and maintaining software for a specific set of users, functions, or organizations. In contrast, commercial off-the-shelf software (COTS) is designed for a broad set of requirements, allowing it to be packaged and commercially marketed and distributed.

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Developing software typically involves the following steps:

• Selecting a methodology to establish a framework in which the steps of software development are applied. It describes an overall work process or roadmap for the project. Methodologies can include Agile development, DevOps, Rapid Application Development (RAD), Scaled Agile Framework (SAFe), Waterfall, and others.
• Gathering requirements to understand and document what is required by users and other stakeholders.
• Choosing or building an architecture as the underlying structure within which the software will operate.
• Developing a design around solutions to the problems presented by requirements, often involving process models and storyboards.
• Building a model with a modeling tool that uses a modeling language like SysML or UML to conduct early validation, prototyping, and simulation of the design.
• Constructing code in the appropriate programming language. Involves peer and team review to eliminate problems early and produce quality software faster.
• Testing with pre-planned scenarios as part of software design and coding — and conducting performance testing to simulate load testing on the application.
• Managing configuration and defects to understand all the software artifacts (requirements, design, code, test) and build distinct versions of the software. Establish quality assurance priorities and release criteria to address and track defects.
• Deploying the software for use and responding to and resolving user problems.
• Migrating data to the new or updated software from existing applications or data sources if necessary.
• Managing and measuring the projec t to maintain quality and delivery over the application lifecycle, and to evaluate the development process with models such as the Capability Maturity Model (CMM).

The steps of the software development process fit into application lifecycle management (ALM). The IBM® Engineering Management solution is a superset of ALM that enables the management of parallel mechanical, electrical, and software development.

• Requirements analysis and specification
• Design and development
• Maintenance and support

Software development process steps can be grouped into the phases of the lifecycle, but the importance of the lifecycle is that it recycles to enable continuous improvement. For example, user issues that surface in the maintenance and support phase can become requirements at the beginning of the next cycle.

Software development is also important because it is pervasive. As IBM vice president and blogger Dibbe Edwards points out: “Software has emerged as a key differentiator in many products — from cars to washing machines to thermostats — with a growing Internet of Things connecting them.”

A few examples:

• Soul Machines (link resides outside ibm.com) uses software to create artificial online advisors that improve customer service and efficiency. The advisors have human faces, expressions and voices that react intelligently, empathetically, and efficiently to customer questions and needs. They can answer over 40 percent of customer inquiries without human intervention — and they learn from their interactions to improve over time. Using IBM Watson Assistant to incorporate artificial intelligence (AI) capabilities into the development process, Soul Machines can create and roll out an artificial advisor in about 8 to 12 weeks.
• “This is a race,” says Erik Bak-Mikkelsen. “We have to keep up with what’s happening in the market.” Bak-Mikkelsen is head of cloud operations at car2go (link resides outside ibm.com). He understands that delivering new features and functions to car2go’s ride-sharing apps and vehicles is key to getting and staying ahead. To do so, car2go moved its development operations to a managed-services cloud and adopted a DevOps development model. The result is accelerated development cycles, faster time to market and the capability to scale for future growth.
• Working with electrical power lines can be deadly. To stay safe engineers set electrical “lockouts” using physical tags and padlocks to divert power from work locations. French energy company Enedis (link resides outside ibm.com) worked with IBM Garage for Cloud to develop software that instruments these locks and tags and ties them into a shared network. Tags and locks detect each time that they are removed from an engineer’s van and communicate the time and geo-location. As the engineer attaches the locks, their location is recorded on a digital map. All stakeholders share a view of the map to ensure safety, reduce downtime and facilitate repairs. The IBM Cloud Garage collaborative development approach enabled Enedis to develop field-ready prototypes in three months.

Using software development to differentiate brands and gain competitive advantage requires proficiency with the techniques and technologies that can accelerate software deployment, quality and efficacy.

• Artificial intelligence (AI):  AI enables software to emulate human decision-making and learning. Neural networks, machine learning, natural language processing and cognitive capabilities present developers and businesses with the opportunity to offer products and services that disrupt marketplaces and leap ahead of the competition. IBM Watson offers developers a way to connect with and use artificial intelligence services as part of their applications through application programming interfaces or APIs . You can also use IBM Watson to improve your product requirements by checking for ambiguity, unclear actors, compound or negative requirements, missing units or tolerances, incomplete requirements, and unspecific quantities.
• Cloud-native development: Cloud-native development is a way of building applications to use cloud environments. A cloud-native application consists of discrete, reusable components that are known as microservices that are designed to integrate into any cloud environment. These microservices act as building blocks and are often packaged in containers . Because of this architecture, cloud-native applications can use cloud environments to improve application performance, flexibility, and extensibility .
• Cloud-based development: Just as IT organizations look to the cloud to improve resource management and cut costs, so do software development organizations. In this way, the cloud can be used as a fast, flexible, and cost-efficient integrated development environment (IDE) or development Platform as a Service (PaaS) . Cloud-based development environments can support coding, design, integration, testing, and other development functions. They can also offer access to APIs, microservices, DevOps and other development tools, services and expertise. 
• Blockchain:  Blockchain is a secure, digitally linked ledger that eliminates cost and vulnerability that is introduced by parties like banks, regulatory bodies and other intermediaries. It is transforming businesses by freeing capital, accelerating processes, lowering transaction costs and more.  Blockchain presents a tremendous opportunity for software development. Developers are working with distributed ledgers and open source Hyperledger (link resides outside ibm.com) technology to change how businesses operate.
• Low code:  Forrester defines low code as: “Products and/or cloud services for application development that employ visual, declarative techniques instead of programming and are available to customers at low- or no-cost in money and training ...”  4 In short, it’s a development practice that reduces the need for coding and enables noncoders or citizen developers to build or help build applications quickly and at lower cost.
• Analytics: Annual demand for data scientists, data developers, and data engineers will reach nearly 700,000 openings by 2020 . The demand signifies how critical it is for companies to gain insight and value from the explosion of data. Accordingly, software developers are integrating advanced analytics capabilities into their applications. Cloud-based services and APIs make it simpler to guide data exploration, automate predictive analytics and create dashboards that deliver new insights and improve decision making.
• Model Based Systems Engineering (MBSE) : In MBSE, software modeling languages are used to perform early prototyping, simulation, and analysis of software designs for early validation. Building designs in MBSE helps you to analyze and elaborate project requirements and move rapidly from design to implementation.  
• Mobile: A key capability for software developers is creating mobile apps with deep connections to data that enriches and elevates user experiences. Forrester has found that “deeply integrating digital/mobile customer data has a strong effect on how customers interact with brands.”
• Agile development breaks requirements into consumable functions and delivers rapidly on those functions through incremental development. A feedback loop helps find and fix defects as functionality continues to deploy.
• Capability Maturity Model (CMM) assesses the proficiency of software development processes. It tracks progress from ad hoc actions to defined steps to measured results and optimized processes.
• DevOps, a combination of development and operations, is an agile-based approach that brings software development and IT operations together in the design, development, deployment, and support of software.
• Rapid application development (RAD) is a nonlinear approach that condenses design and code construction into one interconnected step.
• Scaled Agile Framework (SAFe) provides a way to scale agile methodology to a larger software development team or organization.
• Waterfall, often considered the traditional software development methodology, is a set of cascading linear steps from planning and requirements gathering through deployment and maintenance.

A proven solution for modeling and design activities that helps you deliver higher-quality software and systems faster.

Advanced software version control, workspace management, which is distributed source control and parallel development support for individuals and teams to improve productivity by automatically tracking changes to artifacts. The software enables a virtually unlimited suspend-and-resume feature to handle work interruptions.

Provides connections between IBM Engineering Lifecycle Management tools and 3rd party tools like Git, GitLib, and GitHub for managing version control of software.

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IBM Engineering Lifecycle Management (ELM) can help you embrace an end-to-end management approach to your systems and software development. Conquer complexity from design to execution, unite teams through digital thread, leverage modelling and reuse, harness insights from automated reporting, and confidently operate at scale.

1  Software, Techopedia (link resides outside ibm.com)

2  Embedded software, Wikipedia (link resides outside ibm.com)

3  Software Engineer vs. Software Developer – What’s the Difference? Fullstack Academy (link resides outside ibm.com)

4  The Forrester Wave™: Low-Code development Platforms for AD&D Pros, Q4 201 John R. Rymer, Forrester Research, Inc. 23 October, 2017 (link resides outside ibm.com)

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📚 A curated list of papers for Software Engineers

facundoolano/software-papers

Folders and files, repository files navigation, papers for software engineers.

A curated list of papers that may be of interest to Software Engineering students or professionals. See the sources and selection criteria below.

Von Neumann's First Computer Program. Knuth (1970) . Computer History; Early Programming

• The Education of a Computer. Hopper (1952) .
• Recursive Programming. Dijkstra (1960) .
• Programming Considered as a Human Activity. Dijkstra (1965) .
• Goto Statement Considered Harmful. Dijkstra (1968) .
• Program development by stepwise refinement. Wirth (1971) .
• The Humble Programmer. Dijkstra (1972) .
• Computer Programming as an Art. Knuth (1974) .
• The paradigms of programming. Floyd (1979) .
• Literate Programming. Knuth (1984) .

Computing Machinery and Intelligence. Turing (1950) . Early Artificial Intelligence

• Some Moral and Technical Consequences of Automation. Wiener (1960) .
• Steps towards Artificial Intelligence. Minsky (1960) .
• ELIZA—a computer program for the study of natural language communication between man and machine. Weizenbaum (1966) .
• A Theory of the Learnable. Valiant (1984) .

A Method for the Construction of Minimum-Redundancy Codes. Huffman (1952) . Information Theory

• A Universal Algorithm for Sequential Data Compression. Ziv, Lempel (1977) .
• Fifty Years of Shannon Theory. Verdú (1998) .

Engineering a Sort Function. Bentley, McIlroy (1993) . Data Structures; Algorithms

• On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem. Kruskal (1956) .
• A Note on Two Problems in Connexion with Graphs. Dijkstra (1959) .
• Quicksort. Hoare (1962) .
• Space/Time Trade-offs in Hash Coding with Allowable Errors. Bloom (1970) .
• The Ubiquitous B-Tree. Comer (1979) .
• Programming pearls: Algorithm design techniques. Bentley (1984) .
• Programming pearls: The back of the envelope. Bentley (1984) .
• Making data structures persistent. Driscoll et al (1986) .

A Design Methodology for Reliable Software Systems. Liskov (1972) . Software Design

• On the Criteria To Be Used in Decomposing Systems into Modules. Parnas (1971) .
• Information Distribution Aspects of Design Methodology. Parnas (1972) .
• Designing Software for Ease of Extension and Contraction. Parnas (1979) .
• Programming as Theory Building. Naur (1985) .
• Software Aging. Parnas (1994) .
• Towards a Theory of Conceptual Design for Software. Jackson (2015) .

Programming with Abstract Data Types. Liskov, Zilles (1974) . Abstract Data Types; Object-Oriented Programming

• The Smalltalk-76 Programming System Design and Implementation. Ingalls (1978) .
• A Theory of Type Polymorphism in Programming. Milner (1978) .
• On understanding types, data abstraction, and polymorphism. Cardelli, Wegner (1985) .
• SELF: The Power of Simplicity. Ungar, Smith (1991) .

Why Functional Programming Matters. Hughes (1990) . Functional Programming

• Recursive Functions of Symbolic Expressions and Their Computation by Machine. McCarthy (1960) .
• The Semantics of Predicate Logic as a Programming Language. Van Emden, Kowalski (1976) .
• Can Programming Be Liberated from the von Neumann Style? Backus (1978) .
• The Semantic Elegance of Applicative Languages. Turner (1981) .
• The essence of functional programming. Wadler (1992) .
• QuickCheck: A Lightweight Tool for Random Testing of Haskell Programs. Claessen, Hughes (2000) .
• Church's Thesis and Functional Programming. Turner (2006) .

An Incremental Approach to Compiler Construction. Ghuloum (2006) . Language Design; Compilers

• The Next 700 Programming Languages. Landin (1966) .
• Programming pearls: little languages. Bentley (1986) .
• The Essence of Compiling with Continuations. Flanagan et al (1993) .
• A Brief History of Just-In-Time. Aycock (2003) .
• LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation. Lattner, Adve (2004) .
• A Unified Theory of Garbage Collection. Bacon, Cheng, Rajan (2004) .
• A Nanopass Framework for Compiler Education. Sarkar, Waddell, Dybvig (2005) .
• Bringing the Web up to Speed with WebAssembly. Haas (2017) .

No Silver Bullet: Essence and Accidents of Software Engineering. Brooks (1987) . Software Engineering; Project Management

• How do committees invent? Conway (1968) .
• Managing the Development of Large Software Systems. Royce (1970) .
• The Mythical Man Month. Brooks (1975) .
• On Building Systems That Will Fail. Corbató (1991) .
• The Cathedral and the Bazaar. Raymond (1998) .
• Out of the Tar Pit. Moseley, Marks (2006) .

Communicating sequential processes. Hoare (1978) . Concurrency

• Solution Of a Problem in Concurrent Program Control. Dijkstra (1965) .
• Monitors: An operating system structuring concept. Hoare (1974) .
• On the Duality of Operating System Structures. Lauer, Needham (1978) .
• Software Transactional Memory. Shavit, Touitou (1997) .

The UNIX Time- Sharing System. Ritchie, Thompson (1974) . Operating Systems

• An Experimental Time-Sharing System. Corbató, Merwin Daggett, Daley (1962) .
• The Structure of the "THE"-Multiprogramming System. Dijkstra (1968) .
• The nucleus of a multiprogramming system. Hansen (1970) .
• Reflections on Trusting Trust. Thompson (1984) .
• The Design and Implementation of a Log-Structured File System. Rosenblum, Ousterhout (1991) .

A Relational Model of Data for Large Shared Data Banks. Codd (1970) . Databases

• Granularity of Locks and Degrees of Consistency in a Shared Data Base. Gray et al (1975) .
• Access Path Selection in a Relational Database Management System. Selinger et al (1979) .
• The Transaction Concept: Virtues and Limitations. Gray (1981) .
• The design of POSTGRES. Stonebraker, Rowe (1986) .
• Rules of Thumb in Data Engineering. Gray, Shenay (1999) .

A Protocol for Packet Network Intercommunication. Cerf, Kahn (1974) . Networking

• Ethernet: Distributed packet switching for local computer networks. Metcalfe, Boggs (1978) .
• End-To-End Arguments in System Design. Saltzer, Reed, Clark (1984) .
• An algorithm for distributed computation of a Spanning Tree in an Extended LAN. Perlman (1985) .
• The Design Philosophy of the DARPA Internet Protocols. Clark (1988) .
• TOR: The second generation onion router. Dingledine et al (2004) .
• Why the Internet only just works. Handley (2006) .
• The Network is Reliable. Bailis, Kingsbury (2014) .

New Directions in Cryptography. Diffie, Hellman (1976) . Cryptography

• A Method for Obtaining Digital Signatures and Public-Key Cryptosystems. Rivest, Shamir, Adleman (1978) .
• How To Share A Secret. Shamir (1979) .
• A Digital Signature Based on a Conventional Encryption Function. Merkle (1987) .
• The Salsa20 family of stream ciphers. Bernstein (2007) .

Time, Clocks, and the Ordering of Events in a Distributed System. Lamport (1978) . Distributed Systems

• Self-stabilizing systems in spite of distributed control. Dijkstra (1974) .
• The Byzantine Generals Problem. Lamport, Shostak, Pease (1982) .
• Impossibility of Distributed Consensus With One Faulty Process. Fisher, Lynch, Patterson (1985) .
• Implementing Fault-Tolerant Services Using the State Machine Approach: A Tutorial. Schneider (1990) .
• Practical Byzantine Fault Tolerance. Castro, Liskov (1999) .
• Paxos made simple. Lamport (2001) .
• Paxos made live - An Engineering Perspective. Chandra, Griesemer, Redstone (2007) .
• In Search of an Understandable Consensus Algorithm. Ongaro, Ousterhout (2014) .

Designing for Usability: Key Principles and What Designers Think. Gould, Lewis (1985) . Human-Computer Interaction; User Interfaces

• As We May Think. Bush (1945) .
• Man-Computer symbiosis. Licklider (1958) .
• Some Thoughts About the Social Implications of Accessible Computing. David, Fano (1965) .
• Tutorials for the First-Time Computer User. Al-Awar, Chapanis, Ford (1981) .
• The star user interface: an overview. Smith, Irby, Kimball (1982) .
• Design Principles for Human-Computer Interfaces. Norman (1983) .
• Human-Computer Interaction: Psychology as a Science of Design. Carroll (1997) .

The anatomy of a large-scale hypertextual Web search engine. Brin, Page (1998) . Information Retrieval; World-Wide Web

• A Statistical Interpretation of Term Specificity in Retrieval. Spärck Jones (1972) .
• World-Wide Web: Information Universe. Berners-Lee et al (1992) .
• The PageRank Citation Ranking: Bringing Order to the Web. Page, Brin, Motwani (1998) .

Dynamo, Amazon’s Highly Available Key-value store. DeCandia et al (2007) . Internet Scale Data Systems

• The Google File System. Ghemawat, Gobioff, Leung (2003) .
• MapReduce: Simplified Data Processing on Large Clusters. Dean, Ghemawat (2004) .
• Bigtable: A Distributed Storage System for Structured Data. Chang et al (2006) .
• ZooKeeper: wait-free coordination for internet scale systems. Hunt et al (2010) .
• The Hadoop Distributed File System. Shvachko et al (2010) .
• Kafka: a Distributed Messaging System for Log Processing. Kreps, Narkhede, Rao (2011) .
• CAP Twelve Years Later: How the "Rules" Have Changed. Brewer (2012) .
• Amazon Aurora: Design Considerations for High Throughput Cloud-Native Relational Databases. Verbitski et al (2017) .

On Designing and Deploying Internet Scale Services. Hamilton (2007) . Operations; Reliability; Fault-tolerance

• Ironies of Automation. Bainbridge (1983) .
• Why do computers stop and what can be done about it? Gray (1985) .
• Recovery Oriented Computing (ROC): Motivation, Definition, Techniques, and Case Studies. Patterson et al (2002) .
• Crash-Only Software. Candea, Fox (2003) .
• Building on Quicksand. Helland, Campbell (2009) .

Thinking Methodically about Performance. Gregg (2012) . Performance

• Performance Anti-Patterns. Smaalders (2006) .
• Thinking Clearly about Performance. Millsap (2010) .

Bitcoin, A peer-to-peer electronic cash system. Nakamoto (2008) . Crytpocurrencies

• Ethereum: A Next-Generation Smart Contract and Decentralized Application Platform. Buterin (2014) .

A Few Useful Things to Know About Machine Learning. Domingos (2012) . Machine Learning

• Statistical Modeling: The Two Cultures. Breiman (2001) .
• The Unreasonable Effectiveness of Data. Halevy, Norvig, Pereira (2009) .
• ImageNet Classification with Deep Convolutional Neural Networks. Krizhevsky, Sutskever, Hinton (2012) .
• Playing Atari with Deep Reinforcement Learning. Mnih et al (2013) .
• Generative Adversarial Nets. Goodfellow et al (2014) .
• Deep Learning. LeCun, Bengio, Hinton (2015) .
• Attention Is All You Need. Vaswani et al (2017) .
• Von Neumann's First Computer Program. Knuth (1970) .
• Computing Machinery and Intelligence. Turing (1950) .
• A Method for the Construction of Minimum-Redundancy Codes. Huffman (1952) .
• Engineering a Sort Function. Bentley, McIlroy (1993) .
• A Design Methodology for Reliable Software Systems. Liskov (1972) .
• Programming with Abstract Data Types. Liskov, Zilles (1974) .
• Why Functional Programming Matters. Hughes (1990) .
• An Incremental Approach to Compiler Construction. Ghuloum (2006) .
• No Silver Bullet: Essence and Accidents of Software Engineering. Brooks (1987) .
• Communicating sequential processes. Hoare (1978) .
• The UNIX Time- Sharing System. Ritchie, Thompson (1974) .
• A Relational Model of Data for Large Shared Data Banks. Codd (1970) .
• A Protocol for Packet Network Intercommunication. Cerf, Kahn (1974) .
• New Directions in Cryptography. Diffie, Hellman (1976) .
• Time, Clocks, and the Ordering of Events in a Distributed System. Lamport (1978) .
• Designing for Usability: Key Principles and What Designers Think. Gould, Lewis (1985) .
• The anatomy of a large-scale hypertextual Web search engine. Brin, Page (1998) .
• Dynamo, Amazon’s Highly Available Key-value store. DeCandia et al (2007) .
• On Designing and Deploying Internet Scale Services. Hamilton (2007) .
• Thinking Methodically about Performance. Gregg (2012) .
• Bitcoin, A peer-to-peer electronic cash system. Nakamoto (2008) .
• A Few Useful Things to Know About Machine Learning. Domingos (2012) .

This list was inspired by (and draws from) several books and paper collections:

• Papers We Love
• Ideas That Created the Future
• The Innovators
• The morning paper
• Distributed systems for fun and profit
• Readings in Database Systems (the Red Book)
• Fermat's Library
• Classics in Human-Computer Interaction
• Awesome Compilers
• Distributed Consensus Reading List
• The Decade of Deep Learning

A few interesting resources about reading papers from Papers We Love and elsewhere:

• Should I read papers?
• How to Read an Academic Article
• How to Read a Paper. Keshav (2007) .
• Efficient Reading of Papers in Science and Technology. Hanson (1999) .
• On ICSE’s “Most Influential Papers”. Parnas (1995) .

Selection criteria

• The idea is not to include every interesting paper that I come across but rather to keep a representative list that's possible to read from start to finish with a similar level of effort as reading a technical book from cover to cover.
• I tried to include one paper per each major topic and author. Since in the process I found a lot of noteworthy alternatives, related or follow-up papers and I wanted to keep track of those as well, I included them as sublist items.
• The papers shouldn't be too long. For the same reasons as the previous item, I try to avoid papers longer than 20 or 30 pages.
• They should be self-contained and readable enough to be approachable by the casual technical reader.
• They should be freely available online.
• Examples of this are classic works by Von Neumann, Turing and Shannon.
• That being said, where possible I preferred the original paper on each subject over modern updates or survey papers.
• Similarly, I tended to skip more theoretical papers, those focusing on mathematical foundations for Computer Science, electronic aspects of hardware, etc.
• I sorted the list by a mix of relatedness of topics and a vague chronological relevance, such that it makes sense to read it in the suggested order. For example, historical and seminal topics go first, contemporary internet-era developments last, networking precedes distributed systems, etc.

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Software Development: Integrated Perspective Essay

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

Risk Analysis in Software Development

Software development outsourcing.

Risk analysis is one of the most important parts of software development. On the one hand, it is a business-level tool that serves as a possibility to ensure that all the decisions are supported by evidence. On the other hand, they provide the developers with critical information regarding the vulnerabilities of the developed software and may protect the team from being exposed to numerous threats (Merkow & Raghavan, 2010). There are several advantages and disadvantages that can be associated with risk-analysis methodologies. Within the framework of modern software design, developers have to deal with quite a few limitations, so it is critical to analyze risks before getting to the development process. In other words, the ability to take into consideration typical risk definitions may be one of the core characteristics of high-quality software and efficient software risk assessment. In order to perform eminent risk assessment, the developers have to identify, rank, and mitigate all the risks that they find throughout the way. Overall, risk analysis is a complex process that has to be completed step by step so as to go in line with the development lifecycle.

The two categories of evidence that the results of risk analysis have to dwell on include initial requirements and testing. Due to the multifaceted nature of risk analysis, it cannot be performed solely by the design team (Highsmith, 2013). It is a specialized subject that requires the understanding of business influence, legal proceedings, and the business model that has to be integrated into the software. This kind of approach allows the team to make assumptions when necessary and prevent the escalation of risks. During the next step, security specialists go through the list of assumptions completed by the team and compile a revised version of the list that consists of the most critical instances of threats. Nonetheless, modern software design hardly aligns with the traditional views of risk analysis. This is why the predictions are expected to provide statistically relevant results that can serve as a platform for the future risk mitigation (Davis, 2013). It is safe to say that software design should be one of the key aspects facilitating the process of risk analysis. Knowing that not a single software application is safe when it comes to vulnerabilities, one should perceive risk assessment as a tool that impacts the reputation of developers. All of the potential threats can be identified by means of decomposing the application and dividing it into a series of major components. The significance of risk analysis cannot be underestimated because it influences software development even at the architectural level.

It is a rather common situation when highly technical development environments are subject to outsourcing. Nonetheless, this kind of approach tends to intensify the issues. Even if not taking into consideration the issues with management, the problem of communication becomes a key challenge for the majority of project managers that have to deal with outsourcing. One of the ways to mitigate the issues of software development outsourcing is to align the development practice with a testing framework (Clarke & O’Connor, 2012). Regardless of the size of the project, the outsourcing party can comply with the initial schedule and instructions by means of referring to the original testing framework from time to time. When a company needs to outsource, developing such framework should become an essential component of the project. On a bigger scale, such approach is going to validate the original design and all the milestones created by the developers. There may be other problems transpiring throughout the process of outsourcing because the core two reasons for the latter are saving money and time.

This may negatively affect the development of software design and outshine the significance of working code. The concept of software development lifecycle (SDLC) can accommodate outsourcing only in the case where a number of crucial points are taken into consideration. For instance, the developers may be interested in implementing certain security activities that would safeguard the application on the way from requirements to the final release. According to Paul (2011), the choice of SDLC models should be conducted throughout the requirements phase so as to perform a number of decomposition activities. From the point of view of security and resiliency needs, the concepts that have to be addressed first include data classification, subject-object modeling, and threat modeling. This kind of support of the SDLC facilitates the process of completing security activities.

It also hints at the fact that the development phase has to include recurrent code inspections because, without software assurance controls, proper software development outsourcing is impossible (Volter, Stahl, Bettin, Haase, & Helsen, 2013). Throughout the testing phase of SDLC, the team has to evaluate the efficiency of the existing software assurance controls and perform regression testing. Another important issue that has to be addressed by outsourcing software developers is the concept of user acceptance. It has to be one of the most important factors in terms of both functionality and security. SDLC has to be supported by numerous deployment activities in order to ensure that the essential components of the software development process are secured. All the developed software items have to undergo post-deployment certification to become valid and vulnerability-proof. In this particular case, all the associated data shall be either archived or disposed of in order to protect the developed software.

Clarke, P., & O’Connor, R. V. (2012). The situational factors that affect the software development process: Towards a comprehensive reference framework. Information and Software Technology , 54 (5), 433-447.

Davis, A. (2013). Just enough requirements management: Where software development meets marketing . Boston, MA: Addison-Wesley.

Highsmith, J. (2013). Adaptive software development: A collaborative approach to managing complex systems . Boston, MA: Addison-Wesley.

Merkow, M. S., & Raghavan, L. (2010). Secure and resilient software development. Boca Raton, FL: CRC Press.

Paul, M. (2011). Software security in a flat world. ICS2. Web.

Volter, M., Stahl, T., Bettin, J., Haase, A., & Helsen, S. (2013). Model-driven software development: Technology, engineering, management . Hoboken, NJ: John Wiley & Sons.

• The Importance of Managing the Project Lifecycle to Achieve Successful Project Outputs
• Lean Six Sigma and Software Development Process
• Software Development Lifecycles
• Software Development Life Cycle
• Software Testing Profession
• Software Testing Tools: ZAP, Testing Anywhere, and ThreadFix
• Commercial Off-the-Shelf Software
• Concept of the Network Virtualization
• Chicago (A-D)
• Chicago (N-B)

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Bibliography

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Student Essays

Essay on Software Engineering | I Want to be Software Engineer

Software Engineering is the domain that is related with building software, creating solutions, applications etc for daily life. Software Engineering is  of tremendous importance in today’s life. Read the following Essay on Software Engineering, why I love to a software engineering and Importance of Software Engineering for the growth and development of India

Essay on Software Engineering | Importance of Software Engineering | Why I Love it

I want to be a software engineer because it is a profession that combines my interests in technology, problem solving, and working with people. As a software engineer, I would have the opportunity to work on a variety of projects, using different programming languages and tools. I would also be able to collaborate with other engineers to design and build new applications or improve existing ones.

I Love Software  Engineering

Software engineer, to me, is an art, a creativity and intelligent skills to breath life into the code and build applications to solve the day to day affairs. It is a passion to work with 0s and 1s and give them a meaning which can be understood by the machines as well as humans. In simple terms, it is like being a architect but instead of buildings, we design and construct software. We don’t just write code, we design systems and software that are scalable, constructive and user friendly.

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Importance of Software Engineering these days

To me, the role of software engineering is great. It is expanding into every domain our lives. The fast growth of IT industry has given a tremendous push to the software engineering. It is one of the most challenging, responsible and important job in today’s scenario. I think that every individual should have at least some basic knowledge about software engineering as it will be very useful in our day to day lives.

My Goals as Software Engineer

I want to achieve a lot as a software engineer. I want to be a part of the team that designs and develops new applications. I also want to contribute to improving existing applications. I want to work on projects that are challenging and interesting, and that have a positive impact on people’s lives.

Software engineering can greatly help the growth and development of our country. Firstly, it can help in the area of education. There are many applications and software that can be used to improve the teaching and learning process. Software engineering can also help in the area of governance. There are many applications that can be used to improve the efficiency of government departments.

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The IT industry is one of the biggest employers in our country. Software engineering can help in the development of this industry, and in turn, create more employment opportunities. In conclusion, I would like to say that software engineering is a very important profession, and I am very interested in it. I believe that it has a lot of potential to help our country grow and develop. Thank you.

The Mythical Month Essay on Software Engineering:

Welcome back to our discussion on “The Mythical Man Month”. In the previous section, we talked about the main points of Fred Brooks’ influential essay on software engineering. Now, let’s delve deeper into some interesting background information that will not only add to your knowledge but also give you a better understanding of the concepts discussed in the essay.

Firstly, let’s explore the title of the essay itself. The term “The Mythical Man Month” was coined by author Fred Brooks, who derived it from an old saying – “adding more manpower to a late software project makes it later”. This concept is based on the idea that adding more people to a project will not speed up its completion, but rather slow it down due to communication and coordination issues.

Furthermore, it’s important to note that The Mythical Man Month was published in 1975, a time when software engineering was still a relatively new and evolving field. Brooks’ essay served as a wake-up call for the industry, highlighting the challenges and complexities involved in managing large-scale software projects.

Moving on, let’s take a closer look at some of the key themes discussed in the essay. One of the major points that Brooks emphasizes is the concept of conceptual integrity. According to him, a successful software project requires a unified and consistent design approach, rather than being pieced together by individual components. This idea holds true even today, with many modern software development methodologies emphasizing on integration and collaboration.

Another important aspect highlighted in The Mythical Man Month is the concept of time estimation in software projects. Brooks argues that accurately predicting the time required for a project is incredibly difficult, and even experienced developers tend to underestimate this aspect. This can lead to missed deadlines and an overall delay in project completion.

Overall, The Mythical Man Month remains a must-read for anyone involved in software engineering or project management. Its timeless insights and lessons continue to hold relevance in today’s fast-paced technological landscape. So, if you haven’t already, make sure to add this influential essay to your reading list! So, keep learning and exploring the fascinating world of software engineering. See you in the next section! # Keep Learning! # Happy Coding!

Essay on Importance of Software Engineering:

Software engineering has become an integral part of our daily lives. It is the backbone of modern technology and plays a crucial role in shaping our future. From smartphones to self-driving cars, software engineering has revolutionized the way we live, work, and communicate.

But what exactly is software engineering? In simple terms, it is the application of principles, techniques, and tools to design, develop, and maintain software systems. It involves a systematic and disciplined approach to building high-quality, reliable, and efficient software products.

Software engineering is not just about writing code; it also involves understanding the needs of users, analyzing complex problems, designing solutions, testing for bugs and errors, and continuously improving the software. In today’s fast-paced world where technology is constantly evolving, software engineers are constantly facing new challenges and pushing the boundaries of what is possible.

One of the key benefits of software engineering is its ability to streamline processes and automate tasks. With the use of sophisticated algorithms and programming languages, software engineers can create efficient and accurate systems that save time, reduce errors, and increase productivity. This is especially crucial in industries such as healthcare, finance, and transportation where the stakes are high and accuracy is paramount.

Moreover, software engineering has also played a significant role in promoting innovation and entrepreneurship. With the rise of startups and tech companies, there is a growing demand for skilled software engineers who can bring new ideas to life. This not only drives economic growth but also creates job opportunities for individuals with diverse backgrounds.

However, with advancements in technology and increasing reliance on software, the importance of software engineering goes beyond just improving our daily lives. It also has a profound impact on important global issues such as climate change, healthcare, and education. For instance, software engineers are developing applications and programs to analyze and predict weather patterns, manage medical records, and create interactive learning platforms.

In conclusion, software engineering is an essential field that continues to shape our world in countless ways. It not only enhances our daily lives but also contributes to the betterment of society as a whole. As technology continues to advance, the role of software engineering will become even more crucial and we must continue to invest in this field for a brighter future

Short Essay on Future of Software Engineering:

The field of software engineering is constantly evolving and growing, with new technologies and techniques emerging all the time. As we move into the future, it’s important to consider what changes and advancements we can expect in the world of software engineering.

One major trend that we can expect to continue in the future is the increasing use of artificial intelligence (AI) and machine learning in software development. AI and machine learning are already being used in many areas of software engineering, from automated testing to data analysis and prediction. As these technologies continue to improve, we can expect them to play an even bigger role in the creation and maintenance of software systems.

Another key area of development for the future of software engineering is the increasing focus on user experience (UX). With more and more people using technology in their daily lives, the demand for intuitive, user-friendly software is only going to continue to grow. This means that software engineers will need to prioritize UX design and constantly find ways to improve the user experience of their products.

In addition, there will likely be a shift towards more collaborative and agile methods of software development. As teams become more diverse and distributed, the ability to work together effectively and adapt quickly will become essential. Agile methodologies such as Scrum and Kanban will continue to gain popularity, allowing teams to deliver high-quality software in a timely manner.

Security will also remain a top concern for the future of software engineering. With cyber attacks becoming more sophisticated and common, it’s crucial that software engineers prioritize security measures in their development processes. This may include implementing secure coding practices, conducting regular security audits, and staying up-to-date on the latest security protocols.

Finally, as technology continues to advance at a rapid pace, software engineers will need to constantly adapt and learn new skills in order to stay relevant. Continuous learning and professional development will be key for success in this field.

In conclusion, the future of software engineering is exciting and full of potential. With advancements in AI, UX design, collaboration methods, security measures, and continuous learning, the possibilities are endless. As the demand for efficient and user-friendly software continues to grow, it’s up to software engineers to stay ahead of the curve and shape the future of this ever-evolving field.

Why Study Engineering Essay:

Software engineering is a rapidly growing field that has become increasingly important in today’s technology-driven world. As technology continues to advance at an ever-increasing pace, the need for skilled software engineers also rises. In this short essay, we will discuss some of the key reasons why studying software engineering can be a smart and lucrative choice.

One of the main reasons to study software engineering is the abundance of job opportunities in the field. With the increasing demand for software developers, there is no shortage of job openings and career growth potential in this industry. Whether you are interested in working for a large corporation, a small startup, or even as a freelancer, there are countless opportunities available for software engineers.

Additionally, software engineering offers flexibility in terms of work environment and location. Due to the nature of the work, many software engineers have the option to work remotely or even start their own businesses. This flexibility allows for a better work-life balance and can provide more opportunities for travel and personal growth.

Moreover, studying software engineering can also lead to a highly lucrative career. As technology continues to advance, companies are willing to pay top dollar for skilled software engineers who can design and develop innovative solutions. This means that software engineers often enjoy competitive salaries, as well as opportunities for bonuses and other benefits.

Another compelling reason to study software engineering is the ability to make a tangible impact on the world. In today’s society, technology plays a crucial role in almost every aspect of our lives. By studying software engineering, you have the opportunity to create and develop solutions that can improve people’s lives, whether it be through developing new medical technology or creating a more user-friendly app.

Essay on 10 Reason to Become a Software Engineering:

Are you considering becoming a software engineer but not sure if it’s the right career path for you? With advancements in technology and the ever-growing demand for software development, becoming a software engineer can be a lucrative and fulfilling career choice. In this essay, we will explore 10 reasons why you should consider becoming a software engineer.

Reason #1: High Demand

The demand for software engineers is continuously increasing as technology becomes an integral part of our daily lives. According to the U.S. Bureau of Labor Statistics, employment of software developers is projected to grow 22% from 2019 to 2029, much faster than the average for all occupations. This high demand leads to a stable job market and excellent career opportunities for software engineers.

Reason #2: Lucrative Salary

With high demand comes excellent compensation. Software engineers are one of the highest-paid professionals globally, with an average salary of over $100,000 per year in the United States. This high salary is a reflection of the value and importance placed on software development in today’s society.

Reason #3: Versatile Skills

One of the most attractive aspects of becoming a software engineer is the versatility of skills acquired. As a software engineer, you will learn various programming languages and methodologies that can be applied in different industries. This versatility allows for career growth and mobility, making it an excellent choice for those who enjoy learning new things.

Reason #4: Creativity and Problem-Solving

Software engineering is a highly creative and innovative field. As a software engineer, you will be tasked with finding solutions to complex problems using your creativity and logical thinking skills. This constant challenge keeps the job interesting and allows for personal and professional growth.

Reason #5: Continuous Learning

In today’s rapidly evolving tech industry, learning never stops. Software engineers are constantly updating their skills and keeping up with the latest technologies to stay competitive in the job market. This continuous learning ensures that the work is always engaging and challenging.

Reason #6: Flexibility

Software engineering offers a high level of flexibility, both in terms of work schedule and location. With the rise of remote work opportunities, software engineers can find employment anywhere in the world and have a flexible work schedule that fits their lifestyle.

Reason #7: Impactful Work

Software engineers have the power to make a significant impact on society. From developing life-saving medical software to creating innovative solutions for global issues, software engineering allows individuals to use technology for good and make a positive difference in the world.

Reason #8: Collaboration

Software development is often a collaborative effort, and this fosters a supportive and teamwork-oriented work environment. As a software engineer, you will have the opportunity to work with other talented individuals from diverse backgrounds, creating an open and inclusive workplace.

Reason #9: Constantly Evolving Field

Software engineering is a field that is constantly evolving, making it an exciting career choice for those who enjoy adapting to change and embracing new technologies. With the rise of artificial intelligence, virtual reality, and other emerging technologies, software engineering will continue to be a dynamic and cutting-edge field.

Reason #10: Job Satisfaction

Last but not least, becoming a software engineer can lead to high job satisfaction. The ability to continuously learn, solve problems, make an impact, and work in a collaborative environment can result in a fulfilling and rewarding career.

In conclusion, becoming a software engineer has many advantages, including high demand, lucrative salary, versatile skills, creativity and problem-solving opportunities, continuous learning, flexibility, impactful work, collaboration, constantly evolving field, and job satisfaction. If you are passionate about technology and enjoy challenging yourself intellectually while making a difference in the world, then becoming a software engineer may be the perfect career path for you. So don’t hesitate and take the leap into this exciting and growing field! With hard work and dedication, you can achieve success as a software engineer.

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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Moving past gen AI’s honeymoon phase: Seven hard truths for CIOs to get from pilot to scale

A generative AI reset: Rewiring to turn potential into value in 2024

Implementing generative AI with speed and safety

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Connect and protect every network.

​​Connect and secure networks of any scale—from the edge to the cloud.

​Discover smarter, more secure routing

Build an intelligent, self-defending network with advanced analytics, automated provisioning, and integrated security.  

​Protect your users and data

​Boost security from the WAN to the cloud, with trusted authentication, robust encryption, and granular segmentation.  

Accelerate your deployment

​Deploy integrated network services on demand—anywhere, anytime.

Transform application experience

Get in-depth analytics, visibility, and control to make excellent application experience a cornerstone of your operations.

​Centralize network management

Easily deploy SD-WAN and security while maintaining policy across thousands of sites.

Build a resilient and secure SD-WAN.

Future-ready routers for every network

​cisco catalyst 8300 series edge platforms​.

Get SASE-ready with SD-WAN, cloud-native agility, multilayer security, flexible routing, and edge intelligence. 

Cisco Catalyst 8200 Series Edge Platforms

Move to secure access service edge (SASE) with secure and scalable SD-WAN and intelligent multicloud connectivity. 

Cisco 1000 Series Integrated Services Routers 

Boost performance with advanced security, multicloud access, and wireless capability—in one device. 

Cisco Meraki MX security and SD-WAN appliances

Build a complete SD-branch on a cloud-first platform with secure SD-WAN, access, and IoT.

Fixed wireless access devices

Deliver secure, reliable, and scalable connectivity for remote and branch offices.

Hybrid work

Cisco remote worker gateways.

Experience reliable connectivity with enterprise Wi-Fi access at home without the need for a VPN.

​Industrial

Cisco catalyst ir1100 rugged series routers.

Securely connect remote industrial operations with this rugged, compact, and modular SD-WAN-enabled router.

Cisco Catalyst IR1800 Rugged Series Routers

Speed digitization at the mobile edge, with high performance and industry-leading flexibility.

Cisco Catalyst IR8100 Heavy Duty Series Routers

​​Unite your outdoor edge with this IP67-rated and SD-WAN-enabled router that’s fully modular.  

Cisco Catalyst IR8300 Rugged Series Routers

Deliver peak industrial networking and SD-WAN performance with rugged all-in-one routing and switching.

Service providers

Cisco 8000 series routers.

Deliver the performance and scale to build the internet of the future with cloud-enhanced systems.

Cisco Network Convergence System 5700 Series Routers

Scale with high-density 400G routers for long-term growth and segment routing for SLA-based services.

Cisco ASR 9000 Series Aggregation Services Routers

Support the application performance required to power your services with scalable routing systems.

Cisco Network Convergence System 500 Series Routers

Simplify your access network and converge services with these secure and programmable routers.

Small business

Streamline your small business with secure SD-WAN, access, and IoT on a cloud-first platform.

​Cisco 1000 Series Integrated Services Routers

​Boost performance with advanced security, multicloud access, and wireless capability—in one device.

​​​Cisco 900 Series Integrated Services Routers ​

Combine WAN, switching, security, and advanced connectivity options in a compact, fanless platform.

Cisco Catalyst 8000V Edge Software

Optimize applications and enhance user experience with secure, multicloud connectivity.

Cisco Meraki vMX appliances

Securely extend SD-WAN to public and private cloud infrastructure-as-a-service (IaaS) environments in three clicks.

Cisco IOS XRd Virtual Router

Experience greater agility, improved network efficiency, and lower costs with virtual and containerized routers.

Find a fast route to value

Power your network with secure and reliable experiences for campus, branch, edge, and data center.

Explore routing solutions

Cisco catalyst sd-wan.

Lower costs and complexity, and enrich the user experience at your branches.

Secure access where users and applications reside, with one cloud-native service.

For service providers

Deliver quality services fast and easily with advanced network automation.

Support to get you started

Cisco Success Tracks

Make the most of your IT investment

Optimize the value of your routing solutions for faster results, with digital insights and services expertise.  

Business Critical Services

Move your business forward faster

Modernize IT environments and boost agility with analytics-driven advisory services.  

Find your router

Compare and contrast Cisco routers of all types and sizes. Quickly find the router to meet your needs.