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I used to measure coding progress by how much code I produced. But a larger pile of code is not proof of better coding. Getting better means making sounder decisions: choosing what to build, understanding the code you inherit, and making changes that are clear, testable, and maintainable. Writing more can be useful practice—but volume alone tells you little about whether the work is any good.
Why counting lines or commits misses the point
Output is visible and easy to count. Judgment is harder to see. A feature that takes fewer lines because its behavior is simpler may be a better result than a larger implementation. A careful change to an existing system may teach more than starting another project from scratch. And code that works once but is difficult to understand or safely change can create work for the next person—including your future self.
That does not make writing code unimportant. You need to write it to practice implementation. The mistake is treating the amount written as a reliable measure of what you learned or how useful the result is. Better questions are: Did I understand the problem? Can I explain why this design fits? Can I tell whether the change works? Will someone else be able to maintain it?
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A 2022 Google study examined developers at Google and found that perceived code quality, technical debt, infrastructure tools and support, communication, goals and priorities, and organizational change and process were linked to perceived productivity. In a lagged analysis, increases in perceived code quality tended to come before increases in perceived productivity—not the other way around. The authors wrote: “We find that increases in perceived code quality tend to be followed by increased developer productivity, but not vice versa, providing the strongest evidence to date that code quality affects individual developer productivity.”
That is meaningful evidence, but it is bounded: the study concerns Google developers and perceived productivity. It does not establish a universal formula for improving an individual programmer, or prove that any one practice guarantees skill growth. It does offer a useful corrective to the idea that speed or volume is the whole story: quality can shape how productive work feels and how much it enables later.
What getting better can look like in practice
Improvement is easier to see when an activity builds a specific capability and gives you a way to check the result. These options are not a ranked training plan; choose based on what you want to get better at.
Build a small feature with a clear outcome
Pick a problem small enough to finish, and define what a user should be able to do when it is done. This keeps the exercise grounded in a result rather than a target number of files or lines. Afterward, check whether the feature behaves as intended and whether its implementation is understandable.
Read and maintain existing code
Trace how a behavior works before changing it. Then make a focused improvement, such as simplifying a confusing branch or correcting a defect. This exercises the judgment needed to work safely in a codebase you did not design from scratch.
Write tests and investigate failures
A test gives you a concrete check on behavior; a failing test or bug report gives you a problem to reason through. Use the failure to trace causes, form a hypothesis, and verify the fix. The feedback is more useful than simply adding code and assuming it works.
Review code and explain your reasoning
Review asks you to assess code for clarity, behavior, and maintainability—not just whether it looks familiar. Google Research’s 2021 field experiment examined 5,217 reviews involving 300 professional engineers at one company. It describes review as a way to support software quality and share coding practices, while also finding that anonymous reviewers could often guess authors’ identities and that anonymity could hinder high-bandwidth offline conversation. Review can create opportunities to learn, but it does not make every review equally instructive.
Rank #3
Reduce unnecessary complexity
Revisit a solution and ask what can be removed or made easier to follow without changing the required behavior. The aim is not minimal code at any cost; it is code whose structure helps a reader understand the problem and make the next change safely.
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Not every obstacle to doing good work is an individual skill gap. DORA’s engineering-capability model includes code maintainability, documentation quality, a climate for learning, fast feedback, continuous integration, and test automation. Its delivery measures include change lead time, deployment frequency, change fail percentage, and failed deployment recovery time. These measures help describe how an engineering organization delivers software; they are not a direct score of one person’s learning.
Developer experience can affect whether there is room to do careful work. GitHub’s January 2024 summary of research conducted with DX across more than 20 industry-diverse companies reported that blocking time for deep work was associated with 50% more productivity, intuitive processes with 50% more innovation, and fast code reviews with 20% more innovation. These are findings reported in that study context, not promised effects for every person or team.
The practical implication is not to blame yourself for every slow task—or to assume that a better tool will automatically make you a better programmer. Notice whether interruptions, confusing processes, missing tests, or slow feedback are preventing you from understanding and improving your work. Those conditions can matter alongside individual practice.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use AI output as material to understand, not a skill score
AI-assisted coding makes it especially tempting to equate a large volume of generated code with progress. DORA’s 2025 report frames AI as an amplifier of an organization’s existing strengths and dysfunctions. The Google Research publication page says the report draws on more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals worldwide. This is organizational context, not evidence that producing more AI-generated code improves an individual’s coding skill.
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If you use AI assistance, treat the output as a change you remain responsible for understanding. Check whether it fits the surrounding code, test the behavior, and make sure you can explain the implementation. The useful question is not how much appeared in the editor, but whether you can evaluate and maintain what is there.
Best Value
A better way to judge progress
Instead of tracking code volume as your main measure, look for signs that your decisions are improving. Can you understand unfamiliar code sooner? Can you make a focused change without breaking unrelated behavior? Can you explain trade-offs, find a bug systematically, and incorporate useful feedback? Can you leave code clearer than you found it?
These are not a universal checklist or a guarantee of advancement. They are more revealing questions than “How much did I write?” because they connect practice to understanding, feedback, and the quality of the result. Writing code remains part of getting better; it is simply not the whole measure.
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