Develop software engineering skill by pairing strong fundamentals with end-to-end practice, thoughtful use of AI tools, and the ability to test, secure, deliver, and explain software. AI fluency matters, but it works best as part of a broader engineering capability—not as a replacement for understanding the system or checking the result.
What software engineering skills matter as AI becomes part of the work?
Software engineering is broader than producing code. A 2025 ACM FSE Companion study by Matthew Kam and co-authors organized the knowledge and skills developers need into four domains: effective use of generative AI, core software engineering, adjacent engineering, and adjacent non-engineering. The authors identified 12 work goals and 75 associated tasks, and mapped skills to points in a six-step workflow. Those are the authors’ organizing model, not a universal competency standard: the study drew on 21 developers experienced with AI-assisted work, a qualitative sample rather than a representative survey. Read the paper.
- Core engineering: programming, data structures, algorithms, design patterns, debugging, and the ability to reason about behavior.
- AI fluency: using generative tools effectively while judging their suggestions and understanding the changes they make.
- Adjacent engineering: work connected to testing, delivery, operations, and security—not just implementation.
- Adjacent non-engineering: communication and other skills needed to understand goals and work with people affected by technical decisions.
Microsoft Research frames AI coding tools as affecting “the processes of building, testing, and delivering software,” rather than code completion alone. Its initiative also identifies developer efficiency, software safety, and potential risks as research areas. Microsoft Research’s AI and Software Engineering Research Initiative was published May 2, 2024.
Why fundamentals still matter
Generated code is useful only if you can tell whether it fits the requirement, works in the surrounding system, and can be changed safely. Debugging and knowledge of data structures help you investigate behavior instead of treating a plausible-looking answer as proof. Kam and co-authors cite a prior finding that developers with under one year of experience took 7–10% longer on some tasks when using AI than when not using it, in some situations. That figure is attributed to the prior study cited in their paper; it is not a general penalty for junior developers and was not a result of Kam et al.’s 21-person sample.
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The paper also recommends foundational coursework in syntax, data structures, algorithms, design patterns, and debugging. That is a reason to keep practicing these subjects, not evidence that one particular course sequence or programming language is best.
A practical practice loop for real engineering work
The steps below synthesize the study’s skill domains and workflow framing with secure-development guidance. The sources do not test this exact sequence as a single learning program, so treat it as a useful routine to adapt—not a validated curriculum.
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- Clarify the requirement. State what the change should do, who it serves, and how you will recognize correct behavior. Note unresolved assumptions before writing code.
- Explore the existing system. Read relevant code, tests, and documentation. Trace how a similar feature works and identify dependencies or constraints.
- Sketch a design. Describe the main components and their responsibilities. Consider a plausible alternative and the trade-offs, such as simplicity, maintainability, or security.
- Use AI for targeted help. Ask it to explain an unfamiliar concept, suggest alternatives, generate scaffolding, or critique your design. Keep the task and constraints clear; do not treat output as verified.
- Implement and verify. Read every change you keep. Run relevant tests, add tests for the intended behavior, and investigate failures rather than asking AI to paper over them.
- Review and reflect. Check the change against the requirement and the surrounding code. Explain why you chose this design, what you rejected, and how you would safely modify it later.
A useful check on whether the work is building your capability is whether you can explain the decision, spot a faulty suggestion, change the code safely, and debug the result without relying on the same model output.
How to use AI without outsourcing your judgment
Use a model to accelerate parts of the work while retaining responsibility for the engineering decisions. For example, ask it to outline two designs, point out edge cases in a test plan, or explain a piece of unfamiliar code. Then compare its answer with the actual requirements, repository, and test results. If you cannot explain a generated change or assess its effects, pause and investigate before accepting it.
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This is a practical approach, not a measured best method. The available evidence does not establish a universal ideal balance between unaided work and AI assistance, or prove that AI use improves an individual learner’s long-term skill.
Build skills beyond implementation
Testing and delivery
Practice writing tests that express expected behavior, interpreting failures, and understanding how changes move toward release. A feature is not complete just because code was generated; it has to behave correctly in context and be deliverable.
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Operations and security
Learn how software behaves in its operating environment and how technical choices affect risk. For work involving AI models or AI-enabled systems, NIST SP 800-218A provides specific secure-development practices and tasks across the life cycle. Published July 26, 2024, it is intended to be used with SSDF 1.1 and covers producers of AI models, producers of AI systems that use those models, and acquirers. It is security guidance for AI-related development, not a complete learning curriculum for every software engineer. See NIST SP 800-218A.
Communication and trade-offs
Practice explaining what a change does, why you chose one approach over another, and what risks or limitations remain. This makes design decisions easier to review and helps align implementation with the underlying need.
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How to choose courses, books, or other learning routes
There is no evidence here ranking degrees, workplace learning, self-directed projects, or courses in a controlled comparison. Judge a learning option by what it lets you do and demonstrate, rather than by format alone.
- Does it include hands-on work with real or realistic code?
- Will you practice fundamentals, testing, and debugging—not just prompt a model?
- Is there feedback, code review, or another way to catch misunderstandings?
- Does it cover relevant work beyond implementation, such as security and delivery?
- If AI is included, must you verify and explain its output?
- Can you show that you understand the result and can continue without the same generated answer?
Books and courses can help explain architecture and design, but pair reading with implementation and review. A community discussion, for example, mentions A Philosophy of Software Design as a resource; that is an anecdotal recommendation, not an independent evaluation of the book.
What the evidence says about AI and engineering work
DORA’s 2025 report describes AI primarily as an amplifier: in the organizational settings it studied, AI magnified strengths in high-performing organizations and dysfunctions in struggling ones. The report’s evidence base included more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals around the world. DORA states, “The research reveals a critical truth: AI’s primary role in software development is that of an amplifier.” This is an organization-level finding, not proof that a particular tool improves every developer’s output. Read DORA’s 2025 State of AI-assisted Software Development Report.
DORA’s AI Capabilities Model makes a related point: “But simply adopting AI tools isn’t a guarantee of success.” Kevin Storer and Derek DeBellis’s 2025 introduction points to technical and cultural practices as factors in realizing benefits. See the DORA AI Capabilities Model.
Together, these sources support building a broad portfolio of capabilities rather than chasing an alleged AI-proof skill. They do not establish a universal course sequence, a single best programming language, or a guaranteed career path.
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