When stack traces lie: a failure mode I keep running into
One persistent failure mode I've seen when using large language models for debugging is their tendency to misinterpret runtime stack traces and propose incorrect root causes. I first encountered this while triaging a production Node.js crash where the model confidently pointed at a logging library as the culprit. The link between what it reported and the actual bug was superficial; the model had anchored on a familiar pattern rather than the real flow. See more general workflow notes at crompt.ai.
In that incident the stack trace was from transpiled code and omitted the original source mapping. The model matched the visible frame to a commonly seen logging error pattern and suggested changing the logger configuration. After applying the suggestion the specific crash stopped reproducing locally but reappeared in load tests as a different failure, which hinted that the original diagnosis was wrong.
How it surfaced in our debugging loop
The mistake first surfaced because I followed the model's suggested patch path under time pressure. I opened an interactive session and iterated on fixes, using the assistant to draft commits and tests via chat. The assistant is helpful for multi-turn exploration but also amplifies confidence; when it insists on a cause, it's tempting to stop digging. Around that phase I was using the assistant through chat to generate quick diffs and test cases; the pointer to the wrong module came from that flow.
Because the initial change made immediate unit tests greener, the error felt addressed. Only after introducing load and integration tests did alternative failure modes appear, because the original root cause lived in an async initialization race unrelated to the logger. The stack frames had been reported from an earlier task boundary, making the visible trace a red herring.
Why models make this mistake
Language models excel at pattern matching and completing familiar debugging narratives; they don't execute or observe your process. When they see a stack frame that resembles a common pattern, they prefer a concise, high-probability explanation. That behavior compounds when traces are noisy or missing source maps. For deeper verification I often use external verification and citation tools to cross-check hypotheses, and I've found a useful place to start is deep research resources.
The model's tendency to produce a single confident answer also interacts badly with multi-turn sessions. Early suggested fixes become anchors for follow-ups, and iterative drafts can diverge subtly from the repository's actual structure. Small mismatches in function names or argument orders in generated patches can pass unit tests yet introduce logic regressions under concurrency.
Practical mitigations and workflow changes
Treat model outputs as hypotheses, not authoritative diagnoses. I now require a short reproduction that maps stack frames to source using source maps, runtime instrumentation, or adding explicit logging lines before applying a structural change. That extra step takes a few minutes but prevents chasing symptoms.
Other changes that helped: insist on code-owner review for AI-suggested patches, add load or integration tests that mimic production timing, and keep a small library of scripts to post-process traces into canonical frames. These pragmatic steps accept that assistants accelerate triage while putting human verification back at the center.
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