Open-source maintainers know the pain: a vague issue report shows up — “It doesn’t work, please fix.” No clear context, no logs, no reproducible steps. Worse, with today’s rise of generative AI, maintainers now face a flood of LLM-generated bug reports that look plausible but waste valuable time to diagnose or refute[^1].
We’ve all been tempted: hit a wall, file an issue, move on. But if we want better software (and faster responses to our own issues), we have to do better.
This post is about how AI can help us become better contributors by augmenting our understanding and letting us write higher-quality issues — even when we’re new to the codebase. I’ll illustrate this using a recent dive into the Serena project.
From “It Crashed” to a Useful Issue: Debugging Serena with Claude Code
Recently I tried out Serena, a coding agent toolkit that provides semantic analysis and editing capabilities by exposing a language server interface[^2]. I wanted Serena to index a project so I could use a language server through Claude Code for an AI-powered development workflow.
In a bit of a meta experiment, I pointed Serena at its own repository — asking Serena to index itself.
Here’s what I did:
- Indexed Serena on its own codebase (using
index-projectas recommended for larger repos). - Activated the project in Claude Code (so Claude would operate on Serena’s repo context).
- Added Serena as an MCP server in Claude Code[^3].
- Ran the /mcp__serena__initial_instructions command to load Serena’s tooling interface.
With everything configured, I tried indexing another project and… 💥 failure. Serena crashed during indexing.
I could’ve filed a classic vague issue: “Serena fails on my repo. Help?”
But instead, I used the setup I had just configured: I asked Claude Code itself, equipped with Serena’s tooling and the indexed Serena repo, to help me debug Serena’s internals.
AI-Powered Code Analysis — The Good and the Limits
Here’s where things got interesting: I asked Claude+Serena questions like:
“Why would Serena crash when indexing? Look at the relevant indexing code and help diagnose.”
Initially, I suspected a common culprit: maybe Serena tried to index large irrelevant files like media or SQL dumps. It seemed plausible; after all, Serena doesn’t exclude files by default.
Claude + Serena helped me dig into the indexer implementation and logs, suggesting that Serena recursively indexes all files without filtering — a known inefficiency.
Armed with that hypothesis, I drafted and filed GitHub Issue #322[^4]: an enhancement request proposing that Serena exclude large unnecessary files during indexing.
But the story didn’t end there.
As I analyzed further, reading feedback from other users and maintainers, it became clear that large files weren’t the actual problem in my case. The real root cause was certain files in the project containing extremely complex code (like nested iterators or pathological regular expressions) that caused Serena’s underlying language server to consume excessive memory and crash.
This nuance matters: Serena isn’t just parsing text; it integrates multiple language servers (via its Solid-LSP component) that can choke on complex constructs even in small files. This is a known challenge: language servers can fail on edge-case code and Serena has to orchestrate them safely[^5].
So my enhancement request (#322) still stands — file exclusions are a good idea to improve robustness — but the immediate problem required a deeper fix in Serena’s architecture: handling how the language servers themselves deal with tricky code.
Why This Workflow Matters for Contributors
This episode is a perfect example of how AI can augment but not replace human judgment in issue reporting:
- AI as a partner for reading unfamiliar code: Claude+Serena helped me navigate an unfamiliar repo and surface potential causes quickly.
- Human oversight is critical: The AI could guide hypotheses but it was my responsibility to verify, cross-reference maintainer feedback, and avoid filing a misleading bug report.
- Contributors can raise the bar: Rather than submitting a vague report, I provided evidence, analysis, and even a proposed solution direction — saving maintainers time and making the report actionable.
Lessons and Takeaways
✅ Better for maintainers:
Detailed, thoughtful issues respect the maintainer’s time and bandwidth.
✅ Better for contributors:
You learn the codebase, improve your diagnostic skills, and build credibility as a contributor.
✅ AI helps — when grounded in context:
Don’t treat LLMs as oracles. Provide them with real project context (like Serena’s own codebase and logs) and treat them as research assistants.
✅ Human validation is non-negotiable:
LLMs can’t distinguish signal from noise without you checking. Avoid adding to the noise — use AI to amplify your understanding, not your shortcuts.
TL;DR
- When encountering a bug in an unfamiliar project, invest a bit of effort to analyze and contextualize before filing an issue.
- Use tools like Claude Code + Serena to assist your analysis — they lower the barrier to understanding a codebase fast.
- Provide actionable details in your issue: logs, hypotheses, and potential fixes.
- Validate AI-generated analysis with your own judgment.
- Maintain a constructive tone: you’re contributing, not just reporting.
References
[^1]: “Maintainers increasingly face AI-generated bug report noise” — Example complaint from maintainers about LLM-driven spam issues.
[^2]: Serena project overview — Serena’s description and capabilities.
[^3]: Serena MCP server integration documentation — How to add Serena as an MCP server in Claude Code.
[^4]: GitHub Issue #322 — Enhancement request for file exclusion during indexing.
[^5]: Discussion from Serena maintainers — Comments on Serena’s reliance on underlying LSP stability and memory issues.