A feature can be implemented exactly as requested and still be wrong. In a Stack Overflow Blog example, stakeholders disagreed over whether users could override default terms and conditions: until that behavior was settled, the problem was not how to code the feature but what the software was supposed to do. That is why building software involves much more than writing code—and why requirements and shared understanding can be harder than implementation, though the balance varies by project.
Why code can be correct and the product still be wrong
Code turns decisions into behavior. If the decisions are unclear, contradictory, or mistaken, a developer can implement them correctly and deliver a system that fails the actual need. The Stack Overflow Blog’s example of disagreement over whether users could override default terms and conditions shows how a seemingly small ambiguity can change what the finished product permits. The article argues that requirements are still defined by people, not resolved by code alone: Stack Overflow Blog, republished December 29, 2023.
This does not mean requirements are invariably the hardest part of every project. A team working in unfamiliar technology, under demanding reliability constraints, or with a changing organization may find other work more difficult. The point is that coding is only one part of delivering software that solves the right problem.
What requirements work actually involves
Requirements work is not just writing a feature list. It means making intended behavior specific enough that a team can build it, test it, and recognize when it is wrong. Useful questions include:
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- Who has the problem, and what are they trying to accomplish?
- What should happen in the ordinary user flow, and what should not happen?
- What happens when input is invalid, unexpected, missing, or late?
- Which exceptions or competing expectations need a decision before implementation?
- What would success look like, and what are the consequences of delaying or not building the feature?
The Stack Overflow article describes a proposed SMS health-survey application where the team had not decided how to interpret invalid or unexpected answers. Pausing to resolve those questions was a useful result: it exposed uncertainty before the team encoded it into a system. An explicit decision to delay or stop can save effort when the product’s behavior or value is not yet understood.
Software work includes keeping context, not just changing files
Even after a requirement is clear, developers need to understand why existing code behaves as it does, what colleagues are changing elsewhere, and how to return to a task after interruption. A Microsoft Research report, Software Development at Microsoft Observed (MSR-TR-2005-140), drew on two surveys and eleven interviews across Microsoft divisions. In that study, 66% of surveyed developers cited understanding the rationale behind code as a problem, 62% cited frequent task switching, and 61% cited awareness of changes elsewhere in code. These are findings from Microsoft developers in 2005, not current estimates for developers generally. They nevertheless illustrate how much software work depends on context beyond the code currently being written: Microsoft Research report.
When context is hard to find, teams can lose time reconstructing decisions, overlook changes, or make inconsistent assumptions. Clear records of important decisions and coordination around changes can help, but documentation alone cannot make priorities or intended behavior coherent.
Culture, reliability, and priorities shape the outcome
Software quality depends on the conditions in which teams work as well as their technical skills. DORA’s 2022 research highlights trust, low-blame culture, and application-development security practices. It reports that teams with low levels of those security practices had 1.4 times the odds of high burnout compared with teams with high levels; teams with high security practices were 1.6 times more likely to have high organizational performance. These are reported associations, not proof that security practices alone cause lower burnout or better performance. DORA also summarizes the finding that “The biggest predictor of an organization’s application-development security practices is cultural, not technical.” DORA Research: 2022.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsMore recent DORA summaries emphasize user focus and delivery fundamentals. The 2024 summary calls user-centricity “the ultimate driver of performance” and reports tradeoffs associated with AI adoption, while stressing practices such as small batches and robust testing. The implication is not that one process guarantees success: user needs, stable priorities, and reliable delivery practices have to work together, with outcomes depending on organizational context. DORA Research: 2024.
AI can speed implementation without settling the hard questions
AI tools can affect how implementation is performed, but generating code does not by itself decide what that code should do, which edge cases matter, or how a team will maintain and operate the result. DORA’s 2025 report describes AI as “an amplifier” of organizational strengths and dysfunctions. Its publisher says the report draws on more than 100 hours of qualitative research and survey responses from nearly 5,000 technology professionals; those figures describe the report’s research scope, not a measured effect of a particular practice. The framing supports a bounded conclusion: changes in coding tools do not remove the need for sound requirements, coordination, and delivery practices. DORA 2025 State of AI-assisted Software Development Report.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Questions to settle before asking whether it can be built
Before committing to implementation, a team can make the work more tractable by agreeing on three things:
- Whose problem is being solved? Identify the users and the task or outcome that matters to them.
- What behavior counts as success? Specify the expected flow and resolve conflicting assumptions, such as whether users may override a default.
- Which exceptions and operational needs matter? Decide how to handle invalid or unexpected input, and consider the reliability, security, and testing needs of the service.
These answers will not make every project easy. They do make clear whether the team is ready to code, needs to learn more, or should change the plan. That is often the work that determines whether the code becomes useful software.
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