Dec 30, 2025·8 min
Bug triage with Claude Code: a practical loop for fast fixes
Bug triage with Claude Code using a repeatable loop: reproduce, minimize, identify likely causes, add a regression test, and ship a narrow fix with checks.
Bug triage with Claude Code using a repeatable loop: reproduce, minimize, identify likely causes, add a regression test, and ship a narrow fix with checks.
Bugs feel random when every report turns into a one-off mystery. You poke at the code, try a few ideas, and hope the problem disappears. Sometimes it does, but you don’t learn much, and the same issue shows up again in a different form.
Bug triage is the opposite. It’s a fast way to reduce uncertainty. The goal isn’t to fix everything immediately. The goal is to turn a vague complaint into a clear, testable statement, then make the smallest change that proves the statement is now false.
That’s why the loop matters: reproduce, minimize, identify likely causes with evidence, add a regression test, implement a narrow fix, and validate. Each step removes a specific kind of guesswork. Skip steps and you often pay for it later with bigger fixes, side effects, or “fixed” bugs that were never actually fixed.
Here’s a realistic example. A user says: “The Save button sometimes does nothing.” Without a loop, you might rummage through UI code and change timing, state, or network calls. With a loop, you first make “sometimes” become “every time, under these exact conditions,” like: “After editing a title, then quickly switching tabs, Save stays disabled.” That single sentence is already progress.
Claude Code can speed up the thinking part: turning reports into precise hypotheses, suggesting where to look, and proposing a minimal test that should fail. It’s especially helpful at scanning code, logs, and recent diffs to generate plausible explanations quickly.
You still have to verify what matters. Confirm the bug is real in your environment. Prefer evidence (logs, traces, failing tests) over a good-sounding story. Keep the fix as small as possible, prove it with a regression test, and validate it with clear checks so you don’t trade one bug for another.
The payoff is a small, safe fix you can explain, defend, and keep from regressing.
Good fixes start with a clean workspace and a single, clear problem statement. Before you ask Claude anything, pick one report and rewrite it as:
“When I do X, I expect Y, but I get Z.”
If you can’t write that sentence, you don’t have a bug yet. You have a mystery.
Collect the basics up front so you don’t keep circling back. These details are what make suggestions testable instead of vague: app version or commit (and whether it’s local, staging, or production), environment details (OS, browser/device, feature flags, region), exact inputs (form fields, API payload, user actions), who sees it (everyone, a role, a single account/tenant), and what “expected” means (copy, UI state, status code, business rule).
Then preserve evidence while it’s still fresh. A single timestamp can save hours. Capture logs around the event (client and server if possible), a screenshot or short recording, request IDs or trace IDs, exact timestamps (with timezone), and the smallest snippet of data that triggers the issue.
Example: a Koder.ai-generated React app shows “Payment succeeded” but the order stays “Pending.” Note the user role, the exact order ID, the API response body, and the server log lines for that request ID. Now you can ask Claude to focus on one flow instead of hand-waving.
Finally, set a stopping rule. Decide what will count as fixed before you start coding: a specific test passing, a UI state changing, an error no longer appearing in logs, plus a short validation checklist you’ll run every time. This keeps you from “fixing” the symptom and shipping a new bug.
A messy bug report usually mixes facts, guesses, and frustration. Before you ask for help, convert it into a crisp question Claude can answer with evidence.
Start with a one-sentence summary that names the feature and the failure. Good: “Saving a draft sometimes deletes the title on mobile.” Not: “Drafts are broken.” That sentence becomes the anchor for the entire triage thread.
Then separate what you saw from what you expected. Keep it boring and concrete: the exact button you clicked, the message on screen, the log line, the timestamp, the device, the browser, the branch, the commit. If you don’t have those yet, say so.
A simple structure you can paste:
If details are missing, ask for them as yes/no questions so people can answer quickly: Does it happen on a fresh account? Only on mobile? Only after a refresh? Did it start after the last release? Can you reproduce in incognito?
Claude is also useful as a “report cleaner.” Paste the original report (including copied text from screenshots, logs, and chat snippets), then ask:
“Rewrite this as a structured checklist. Flag contradictions. List the top 5 missing facts as yes/no questions. Do not guess causes yet.”
If a teammate says “It fails randomly,” push it toward something testable: “Fails 2/10 times on iPhone 14, iOS 17.2, when tapping Save twice quickly.” Now you can reproduce it on purpose.
If you can’t make the bug happen on demand, every next step is guesswork.
Start by reproducing it in the smallest environment that can still show the problem: a local dev build, a minimal branch, a tiny dataset, and as few services turned on as possible.
Write down the exact steps so someone else can follow them without asking questions. Make it copy-paste friendly: commands, IDs, and sample payloads should be included exactly as used.
A simple capture template:
Frequency changes your strategy. “Always” bugs are great for fast iteration. “Sometimes” bugs often point to timing, caching, race conditions, or hidden state.
Once you have reproduction notes, ask Claude for quick probes that reduce uncertainty without rewriting the app. Good probes are small: one targeted log line around the failing boundary (inputs, outputs, key state), a debug flag for a single component, a way to force deterministic behavior (fixed random seed, fixed time, single worker), a tiny seed dataset that triggers the issue, or a single failing request/response pair to replay.
Example: a signup flow fails “sometimes.” Claude might suggest logging the generated user ID, email normalization result, and unique constraint error details, then rerunning the same payload 10 times. If the failure only happens on the first run after deploy, that’s a strong hint to check migrations, cache warmup, or missing seed data.
A good reproduction is useful. A minimal reproduction is powerful. It makes the bug faster to understand, easier to debug, and less likely to “fix” by accident.
Strip away anything that isn’t required. If the bug shows up after a long UI flow, find the shortest path that still triggers it. Remove optional screens, feature flags, and unrelated integrations until the bug either disappears (you removed something essential) or stays (you found noise).
Then shrink the data. If the bug needs a large payload, try the smallest payload that still breaks. If it needs a list of 500 items, see whether 5 fails, then 2, then 1. Remove fields one by one. The goal is the fewest moving parts that still reproduce the bug.
A practical method is “remove half and retest”:
Example: a checkout page crashes “sometimes” when applying a coupon. You discover it only fails when the cart has at least one discounted item, the coupon has lowercase letters, and shipping is set to “pickup.” That’s your minimal case: one discounted item, one lowercase coupon, one pickup option.
Once the minimal case is clear, ask Claude to turn it into a tiny reproduction scaffold: a minimal test that calls the failing function with the smallest inputs, a short script that hits one endpoint with a reduced payload, or a small UI test that visits one route and performs one action.
Once you can reproduce the problem and you have a small test case, stop guessing. Your goal is to land on a short list of plausible causes, then prove or disprove each one.
A useful rule is to keep it to three hypotheses. If you have more than that, your test case is probably still too big or your observations are too vague.
Translate what you see into where it could be happening. A UI symptom doesn’t always mean a UI bug.
Example: a React page shows a “Saved” toast, but the record is missing later. That can point to (1) UI state, (2) API behavior, or (3) the database write path.
Ask Claude to explain likely failure modes in plain language, then ask what proof would confirm each one. The goal is to turn “maybe” into “check this exact thing.”
Three common hypotheses and the evidence to collect:
Keep your notes tight: symptom, hypothesis, evidence, verdict. When one hypothesis matches the facts, you’re ready to lock in a regression test and fix only what’s necessary.
A good regression test is your safety belt. It proves the bug exists, and it tells you when you’ve truly fixed it.
Start by choosing the smallest test that matches the real failure. If the bug only appears when multiple parts work together, a unit test can miss it.
Use a unit test when a single function returns the wrong value. Use an integration test when the boundary between parts is the problem (API handler plus database, or UI plus state). Use end-to-end only when the bug depends on the full user flow.
Before you ask Claude to write anything, restate the minimized case as a strict expected behavior. Example: “When the user saves an empty title, the API must return 400 with message ‘title required’.” Now the test has a clear target.
Then have Claude draft a failing test first. Keep setup minimal and copy only the data that triggers the bug. Name the test after what the user experiences, not the internal function.
Do a quick pass yourself:
Once the test fails for the right reason, you’re ready to implement a narrow fix with confidence.
Once you have a small repro and a failing regression test, resist the urge to “clean things up.” The goal is to stop the bug with the smallest change that makes the test pass for the right reason.
A good narrow fix changes the smallest surface area possible. If the failure is in one function, fix that function, not the whole module. If a boundary check is missing, add the check at the boundary, not across the entire call chain.
If you’re using Claude to help, ask for two fix options, then compare them for scope and risk. Example: if a React form crashes when a field is empty, you might get:
Option A is usually the triage choice: smaller, easier to review, and less likely to break something else.
To keep the fix narrow, touch as few files as possible, prefer local fixes over refactors, add guards and validation where the bad value enters, and keep the behavior change explicit with one clear before/after. Leave comments only when the reason isn’t obvious.
Concrete example: a Go API endpoint panics when an optional query param is missing. The narrow fix is to handle the empty string at the handler boundary (parse with a default, or return a 400 with a clear message). Avoid changing shared parsing utilities unless the regression test proves the bug is in that shared code.
After the change, rerun the failing test and one or two nearby tests. If your fix requires updating many unrelated tests, it’s a signal the change is too broad.
Validation is where you catch the small, easy-to-miss problems: a fix that passes one test but breaks a nearby path, changes an error message, or adds a slow query.
First, rerun the regression test you added. If it passes, run the nearest neighbors: tests in the same file, the same module, and anything that covers the same inputs. Bugs often hide in shared helpers, parsing, boundary checks, or caching, so the most relevant failures usually show up close by.
Then do a quick manual check using the original report steps. Keep it short and specific: the same environment, the same data, the same sequence of clicks or API calls. If the report was vague, test the exact scenario you used to reproduce it.
If you want help staying focused, ask Claude for a short validation plan based on your change and the failing scenario. Share what file you changed, what behavior you intended, and what could plausibly be affected. The best plans are short and executable: 5 to 8 checks you can finish in minutes, each with a clear pass/fail.
Finally, capture what you validated in the PR or notes: which tests you ran, what manual steps you tried, and any limits (for example, “did not test mobile”). This makes the fix easier to trust and easier to revisit later.
The fastest way to waste time is to accept a “fix” before you can reproduce the problem on demand. If you can’t make it fail reliably, you can’t know what actually improved.
A practical rule: don’t ask for fixes until you can describe a repeatable setup (exact steps, inputs, environment, and what “wrong” looks like). If the report is vague, spend your first minutes turning it into a checklist you can run twice and get the same result.
Fixing without a reproducible case. Require a minimal “fails every time” script or set of steps. If it only fails “sometimes,” capture timing, data size, feature flags, and logs until it stops being random.
Minimizing too early. If you strip the case down before you’ve confirmed the original failure, you can lose the signal. First lock the baseline reproduction, then shrink it one change at a time.
Letting Claude guess. Claude can propose likely causes, but you still need evidence. Ask for 2 to 3 hypotheses and the exact observations that would confirm or reject each one (a log line, a breakpoint, a query result).
Regression tests that pass for the wrong reason. A test can “pass” because it never hits the failing path. Make sure it fails before the fix, and that it fails with the expected message or assertion.
Treating symptoms instead of the trigger. If you add a null check but the real issue is “this value should never be null,” you may hide a deeper bug. Prefer fixing the condition that creates the bad state.
Run the new regression test and the original reproduction steps before and after your change. If a checkout bug only happens when a promo code is applied after changing shipping, keep that full sequence as your “truth,” even if your minimized test is smaller.
If your validation depends on “it looks good now,” add one concrete check (a log, a metric, or a specific output) so the next person can verify it quickly.
When you’re under time pressure, a small, repeatable loop beats heroic debugging.
Write the final decision in a few lines so the next person (often future you) can trust it. A useful format is: “Root cause: X. Trigger: Y. Fix: Z. Why safe: W. What we did not change: Q.”
Next steps: automate what you can (a saved repro script, a standard test command, a template for root-cause notes).
If you build apps with Koder.ai (koder.ai), Planning Mode can help you outline the change before you touch code, and snapshots/rollback make it easier to experiment safely while you work through a tricky repro. Once the fix is validated, you can export the source code or deploy and host the updated app, including with a custom domain when needed.
Bug triage is the habit of turning a vague report into a clear, testable statement, then making the smallest change that proves the statement is no longer true.
It’s less about "fix everything" and more about reducing uncertainty step by step: reproduce, minimize, form evidence-based hypotheses, add a regression test, fix narrowly, validate.
Because each step removes a different kind of guesswork.
Rewrite it as: “When I do X, I expect Y, but I get Z.”
Then collect just enough context to make it testable:
Start by confirming you can reproduce it in the smallest environment that can still show it (often local dev with a tiny dataset).
If it’s “sometimes,” try to make it deterministic by controlling variables:
Don’t move on until you can make it fail on demand, or you’re just guessing.
Minimization means removing anything that isn’t required while keeping the bug.
A practical method is “remove half and retest”:
Shrink both steps (shorter user flow) and data (smaller payload, fewer fields/items) until you have the smallest repeatable trigger.
Use Claude Code to speed up analysis, not to replace verification.
Good requests look like:
Then you validate: reproduce locally, check logs/traces, and confirm any test fails for the right reason.
Keep it to three. More than that usually means your repro is still too big or your observations are vague.
For each hypothesis, write:
Pick the smallest test level that matches the failure:
A good regression test:
Make the smallest change that makes the failing regression test pass.
Rules of thumb:
If your fix forces lots of unrelated test updates, it’s probably too broad.
Use a short checklist you can execute quickly:
Write down what you ran and what you didn’t test so the result is trustworthy.
This forces progress and prevents endless “maybe it’s X” debugging.