If you have ever stared at thousands of lines of integration test logs wondering which of the sixteen log files actually contains your bug, you are not alone — and Google now has data to prove it.

A team of Google researchers introduced Auto-Diagnose, an LLM-powered tool that automatically reads the failure logs from a broken integration test, finds the root cause, and posts a concise diagnosis directly into the code review where the failure showed up. On a manual evaluation of 71 real-world failures spanning 39 distinct teams, the tool correctly identified the root cause 90.14% of the time. It has run on 52,635 distinct failing tests across 224,782 executions on 91,130 code changes authored by 22,962 distinct developers, with a ‘Not helpful’ rate of just 5.8% on the feedback received.

The problem: integration tests are a debugging tax

Integration tests verify that multiple components of a distributed system actually communicate to each other correctly. The tests Auto-Diagnose targets are hermetic functional integration tests: tests where an entire system under test (SUT) — typically a graph of communicating servers — is brought up inside an isolated environment by a test driver, and exercised against business logic. A separate Google survey of 239 respondents found that 78% of integration tests at Google are functional, which is what motivated the scope.

Diagnosing integration test failures showed up as one of the top five complaints in EngSat, a Google-wide survey of 6,059 developers. A follow-up survey of 116 developers found that 38.4% of integration test failures take more than an hour to diagnose, and 8.9% take more than a day — versus 2.7% and 0% for unit tests.

The root cause is structural. Test driver logs usually surface only a generic symptom (a timeout, an assertion). The actual error lives somewhere inside one of the SUT component logs, often buried under recoverable warnings and ERROR-level lines that are not actually the cause.

How Auto-Diagnose works

When an integration test fails, a pub/sub event triggers Auto-Diagnose. The system collects all test driver and SUT component logs at level INFO and above — across data centers, processes, and threads — then joins and sorts them by timestamp into a single log stream. That stream is dropped into a prompt template along with component metadata.

The model is Gemini 2.5 Flash, called with temperature = 0.1 (for near-deterministic, debuggable outputs) and topp = 0.8. Gemini was not fine-tuned on Google’s integration test data; this is pure prompt engineering on a general-purpose model.

The prompt itself is the most instructive part of this research. It walks the model through an explicit step-by-step protocol: scan log sections, read component context, locate the failure, summarize errors, and only then attempt a conclusion. Critically, it includes hard negative constraints — for example: if the logs do not contain lines from the component that failed, do not draw any conclusion.

The model’s response is post-processed into a markdown finding with ==Conclusion==, ==Investigation Steps==, and ==Most Relevant Log Lines== sections, then posted as a comment in Critique, Google’s internal code review system. Each cited log line is rendered as a clickable link.

Numbers from production

Auto-Diagnose averages 110,617 input tokens and 5,962 output tokens per execution, and posts findings with a p50 latency of 56 seconds and p90 of 346 seconds — fast enough that developers see the diagnosis before they have switched contexts.

Critique exposes three feedback buttons on a finding: Please fix (used by reviewers), Helpful, and Not helpful (both used by authors). Across 517 total feedback reports from 437 distinct developers, 436 (84.3%) were “Please fix” from 370 reviewers — by far the dominant interaction, and a sign that reviewers are actively asking authors to act on the diagnoses. Among dev-side feedback, the helpfulness ratio (H / (H + N)) is 62.96%, and the “Not helpful” rate (N / (PF + H + N)) is 5.8% — well under Google’s 10% threshold for keeping a tool live. Across 370 tools that post findings to Critique, Auto-Diagnose ranks #14 in helpfulness, putting it in the top 3.78%.

The manual evaluation also surfaced a useful side effect. Of the seven cases where Auto-Diagnose failed, four were because test driver logs were not properly saved on crash, and three were because SUT component logs were not saved when the component crashed — both real infrastructure bugs, reported back to the relevant teams. In production, around 20 ‘more information is needed‘ diagnoses have similarly helped surface infrastructure issues.

Key Takeaways

• Auto-Diagnose hit 90.14% root-cause accuracy on a manual evaluation of 71 real-world integration test failures spanning 39 teams at Google, addressing a problem 6,059 developers ranked among their top five complaints in the EngSat survey.
• The system runs on Gemini 2.5 Flash with no fine-tuning — just prompt engineering. A pub/sub trigger collects logs across data centers and processes, joins them by timestamp, and sends them to the model at temperature 0.1 and top_p 0.8.
• The prompt is engineered to refuse rather than guess. Hard negative constraints force the model to respond with “more information is needed” when evidence is missing — a deliberate trade-off that prevents hallucinated root causes and even helped surface real infrastructure bugs in Google’s logging pipeline.
• In production since May 2025, Auto-Diagnose has run on 52,635 distinct failing tests across 224,782 executions on 91,130 code changes from 22,962 developers, posting findings in a p50 of 56 seconds — fast enough that engineers see the diagnosis before switching contexts.

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