“How did you know the code was wrong?” A junior engineer asked me, and I had no good answer. I had noticed something suspicious; I had not yet explained what I noticed or how to check it.
That gap matters. Experience can help you spot where to look, but a hunch is not proof. The useful answer is a trail of evidence: what the program should do, what it actually did, and which observations support—or weaken—a possible explanation.
What does it mean to know that code is wrong?
Start with behavior, not a feeling about the code. Describe the expected result and the actual result. If you cannot point to a mismatch, you may have a concern about design or readability rather than a demonstrated bug.
For example, a reviewer might say, “This branch looks risky.” That is a useful signal, but not yet an explanation. A more helpful statement identifies the condition, the behavior it produces, and why that conflicts with the requirement. Google’s troubleshooting guidance recommends establishing expected versus actual behavior and finding a way to reproduce the problem where possible.
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How do you turn a hunch into evidence?
Describe and reproduce the symptom
Write down what happened, what you expected, and the conditions under which the difference appears. A small, repeatable case is often more useful than a long account of everything that might be related. As Google SRE’s Troubleshooting Methodology puts it, “Having a solid reproducible test case makes debugging much faster.”
If the failure only occurs intermittently or in production, capture relevant logs and telemetry, along with the inputs and system state that help distinguish one explanation from another. Gathering more data is not automatically progress: useful evidence is evidence that can confirm or rule out a plausible cause.
Trace the relevant path
Follow the behavior through the parts of the system that could produce it. Check the inputs, state changes, branches, and outputs involved in the symptom. Use what you know about the system to narrow the search, but treat that knowledge as a way to choose questions—not as a substitute for checking their answers.
Test explanations against observations
List a few plausible causes and ask what each one predicts. Then look for an observation or controlled test that could separate them. Prefer a test that is narrow and low-risk; change one relevant condition at a time where practical, and compare the result with what the explanation predicts.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11If a test disagrees with your hypothesis, revise the hypothesis. In a complex production system, evidence may support a likely cause without establishing it beyond doubt. Say which is which: “This is the leading explanation because…” is more accurate than claiming certainty the investigation has not earned. Google SRE discusses both the value of testing explanations and the difficulty of definitive proof in complex systems.
How can a code review make that reasoning clear?
Code review is not only a search for obvious failures. Google’s engineering review guidance identifies design, functionality, complexity, tests, naming, comments, style, and documentation as areas reviewers may consider. Naming the relevant concern gives the author something concrete to evaluate.
For a possible correctness issue, explain the behavior you expect, the path or condition that concerns you, and what evidence would resolve the question. If the issue is instead complexity, unclear naming, or missing documentation, say that directly; do not describe a preference as a proven bug.
Google’s review standard says technical facts and data should carry more weight than opinions or personal preferences, and treats review as an opportunity to teach. That makes feedback more useful when it exposes the observation and reasoning behind a judgment rather than asking the author to accept “it feels wrong.”
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What should a mentor say when the answer is not yet certain?
It is fine to say, “I have a suspicion, but I haven’t established the cause yet.” Then make the next step explicit: state the expected and actual behavior, identify what you have observed, and describe the test that would distinguish the leading explanations. This shows a junior engineer how to investigate without presenting intuition as magic.
Experience still has value: it can help you notice patterns and choose promising questions. But the judgment becomes useful to someone else when you can show what prompted it, how you checked it, and where uncertainty remains. The junior’s question forced me to do more than recognize a problem. It made me explain how I knew.
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