Claude Codeによるデバッグ

Effective debugging with Claude Code follows a four-step process: describe symptoms, gather evidence, isolate the cause, and verify the fix. The key difference from manual debugging is that you provide the observations and Claude handles the analysis. This division of labor plays to each party's strengths. When describing symptoms to Claude, be as specific as possible about what you observe versus what you expect. 'The API returns 500' is less useful than 'the POST /api/orders endpoint returns 500 with error "Cannot read property id of undefined" when the request body includes a discount code, but works fine without one.' The additional context often points Claude directly at the root cause. Provide Claude with the full error output, including stack traces, relevant log entries, and any recent changes that might be related. Claude excels at correlating information across multiple sources. A stack trace plus a recent git diff plus a failing test output gives Claude enough data to pinpoint the issue in most cases.

Claude Code is exceptionally good at analyzing log files and error traces. Where a human might need minutes to scan through hundreds of log lines, Claude can process the entire output and extract the relevant entries instantly. Feed it your application logs, server logs, or build output, and ask it to identify the root cause. For complex issues that span multiple services, provide logs from each relevant service. Claude can correlate timestamps, request IDs, and error codes across services to trace the path of a failing request. This is particularly valuable for debugging microservice architectures where the root cause in one service manifests as an error in another. When dealing with intermittent issues, provide Claude with logs from both successful and failing requests. Asking Claude to 'compare these two request logs and identify what differs' often reveals environment-specific factors, race conditions, or data-dependent bugs that are hard to spot manually.

Before fixing a bug, you need to reproduce it reliably. Claude Code can help by analyzing the conditions under which the bug occurs and generating minimal reproduction cases. This is especially valuable for bugs that are hard to reproduce locally, such as those triggered by specific data patterns, timing conditions, or edge cases. Ask Claude to generate a test that reproduces the bug. Start with 'write a test that demonstrates the bug in the applyDiscount function when given an expired discount code.' If Claude writes the test and it passes (meaning the bug is not reproduced), provide additional context about the environment or data conditions. Iterate until the test fails, confirming the reproduction. For UI bugs, Claude can help create specific application states that trigger the issue. Describe the user flow that leads to the bug, and Claude can generate test fixtures, mock data, or Playwright scripts that set up the exact conditions needed for reproduction.

Once the root cause is identified and a reproduction test exists, Claude Code can implement the fix and verify it in a single workflow. The reproduction test serves as a built-in verification: when the test passes after the fix, you have confidence the bug is resolved. Ask Claude to implement the fix and run the reproduction test. Claude will make the code change, execute the test, and report the result. If the test still fails, Claude can iterate on the fix without additional prompting. This tight feedback loop often resolves bugs faster than manual debugging because Claude can try multiple approaches in rapid succession. After the immediate fix, ask Claude to check for similar patterns elsewhere in the codebase. A bug in one place often indicates the same pattern exists in other locations. Claude can scan the entire codebase for the vulnerable pattern and apply preventive fixes before they become production incidents.