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To find the cause of a bug, first make the failure repeatable, then use a test to capture the mismatch, logs to establish what happened, and a debugger to inspect the state and execution path where behavior first diverges. Form a specific cause hypothesis, make a focused change, and rerun the same reproduction and relevant tests. These methods work best together: a test detects a mismatch, while logs and debugger inspection help explain it.
How do I find the cause of a bug?
Use a stable reproduction as your reference point. Record what triggers the problem, what you expected, what actually happened, and the conditions in which it occurred. Then collect evidence until you can identify a particular state transition or decision that explains the difference.
Write down the reproduction
- Record the exact steps, input, request, or data that triggers the issue.
- Describe the expected result and the observed result separately.
- Note relevant conditions, such as runtime, configuration, environment, and data state.
- Say whether the failure happens every time or intermittently.
If possible, reduce the reproduction to the smallest input or sequence that still fails. A smaller case gives you a clearer target for a test and makes debugger observations easier to compare. Apple’s bug-diagnosis guidance likewise recommends establishing reliable reproduction steps before inspecting execution.
Do not assume every intermittent failure can immediately become a deterministic test. For concurrency- or timing-sensitive issues, preserve the timing conditions you know about; stopping execution may change the behavior you are trying to observe.
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How do I debug a failing test?
A failing test shows that actual behavior differs from an encoded expectation. It can make the failure repeatable and, after a fix, serve as a regression check. It usually does not explain why the code produced the wrong result: that requires examining the relevant state and execution path.
Turn the observed mismatch into an assertion
Where practical, encode the triggering input and expected behavior in a focused test. Run it to confirm that it fails for the same reason as the reported bug. If you cannot yet create a useful test—such as with a one-off production issue or a timing-sensitive failure—start with available logs, a trace, crash details, or a minimal reproduction, then add a regression test when the condition is understood well enough.
Use the failure to guide the next investigation, not to guess at a fix. Identify which value, output, or branch violates the expectation, then inspect how that state was produced.
How do I use logs to debug code?
Logs record selected events and values over time without requiring an interactive pause. They are useful for establishing the order of events, comparing a failing run with an expected run, and investigating cases where stopping execution could change timing.
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Log the context that distinguishes the cases
Choose a boundary or decision relevant to the bug, and record enough context to interpret it: the operation or request identifier, relevant inputs or state, and the branch or event being investigated. Focused observations are more useful than dumping every available value, which can bury the signal in noise.
In Visual Studio Code, a logpoint sends a debugger message to the Debug Console without pausing and can include expressions. Its availability depends on the debugger extension. Xcode also supports breakpoint actions that log values and continue execution; Apple notes this can help when pausing would disturb timing in concurrent code. See the VS Code debugging guide and Apple’s Xcode guidance.
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Logs are evidence of what was recorded, not a complete account of everything that happened. Missing timestamps, correlation identifiers, or relevant state can make them misleading; unrecorded events cannot be inferred from silence. Follow your project’s data-handling rules and do not expose secrets or personal data in production logs.
How do I use a debugger to find where a bug happens?
Set a breakpoint before the point where you suspect state becomes wrong, reproduce the issue, and inspect the variables and call stack. Step into a function to examine its internals, step over it when those internals are not relevant, and step out to return to its caller. Look for the first point where a value or branch differs from the contract and the failing test’s expectation.
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A call stack shows active calls; it does not prove which one caused the bad state. A crash or error may be detected in a different location from where the invalid value was created. Inspect relevant callers, arguments, and state at boundaries to trace how execution reached the failure.
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The tools vary by language and runtime. Python’s standard-library pdb documentation describes source-level stepping, stack-frame inspection, conditional breakpoints, and post-mortem debugging. Visual Studio Code includes debugging support for JavaScript, TypeScript, and Node.js; other languages and runtimes require an extension, whose capabilities determine supported launch configurations, breakpoint types, expression syntax, and remote debugging. See VS Code’s debugger documentation and Microsoft’s guide to stepping through code and navigating the call stack. Debugger controls and shortcuts are tool-specific, not universal.
Which should I use: a test, logs, or a debugger?
Choose based on what you need to learn and whether pausing is safe. Tests, logs, and interactive debugging are complementary: a test makes an expected behavior check repeatable, logs capture selected observations over time, and a debugger exposes live execution state.
| Method | What it reveals | Limits | Best use |
|---|---|---|---|
| Tests | Whether a specified behavior meets an expectation; repeatable pass-or-fail evidence. | Usually do not explain the cause by themselves; evidence is limited to encoded cases. | Reproducing a known mismatch and preserving it as a regression check. |
| Logs or logpoints | Selected events and values in sequence. | Show only what was recorded; ordinary log statements can add noise, and debugger logpoint support varies. | Establishing sequence and state, especially when pausing is undesirable. |
| Interactive debugger | Live variables, execution point, call stack, and control flow. | Requires a suitable runtime and debugger setup; pauses can affect timing. | Narrowing where execution first diverges in a reproducible case. |
For an intermittent or concurrent failure, start with observations that preserve execution where possible. For a deterministic failure with a clear expected result, a test can provide a stable check while debugger inspection narrows the cause.
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How do I test whether my bug diagnosis is correct?
State the suspected cause as a causal chain, not just a location: “When condition X occurs, function Y receives value Z, takes branch A, and produces the unexpected result.” Then make the smallest relevant change that should distinguish this explanation from alternatives.
- Apply the focused change that follows from the hypothesis.
- Rerun the original reproduction and confirm that the failure no longer occurs.
- Run the relevant tests to check the affected behavior and nearby expectations.
- If the predicted result does not happen, revise the hypothesis and continue tracing the state rather than treating the change as a fix.
A passing test is evidence for the inputs and assertions it covers, not proof that every input or environment is correct. Apple’s documented workflow similarly calls for retesting after a potential fix and reconsidering the suspected location if the issue remains.
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