Automated Bot Findings Triage Workflow
Route automated reviewer findings by signal quality, fix the high-signal classes at author time, and silence the noisy ones instead of training ignore.
Objective
Keep automated review bots trustworthy by ensuring their findings are worth reading: high-signal rules enforced, low-signal ones tuned out.
Environment
- Automated reviewers (lint bots, security scanners, AI reviewers) commenting on PRs
- Usage data on which findings get acted on
- Authority to configure or disable bot rules
Steps
1. Measure the action rate per rule
Track which bot findings lead to code changes versus dismissal, computing an action rate per rule or finding class.
Expected state: A per-rule action-rate table exists naming the noise.
2. Fix or escalate low-signal rules
For rules below the action threshold, tune the rule, narrow its scope, or disable it — every retained finding should earn its interruption.
Expected state: Low-signal rules are reconfigured or off; the feed's action rate rises.
3. Move high-signal findings earlier
Promote the consistently-acted classes from PR comments to pre-push or editor-time checks, so authors fix them before the interruption.
Expected state: High-signal classes mostly disappear from PR threads by moving earlier.
4. Treat AI-reviewer findings with structured review
For generative reviewer comments, require authors to explicitly accept or reject each with a reason, keeping the signal measurable.
Expected state: AI findings carry explicit resolutions feeding the same action-rate table.
5. Review the feed monthly
Audit action rates monthly, retiring decayed rules and promoting new patterns, keeping the bot feed a signal source.
Expected state: The monthly audit shows a stable or rising overall action rate.
Expected outcome
An automated review feed whose findings get acted on at a high rate, high-signal checks moved to author time, and noise eliminated rather than ignored.
Verification
The overall action rate stays above the agreed threshold; no rule with a persistently near-zero action rate survives a monthly audit.
A bot feed everyone ignores is negative automation
Automated findings earn trust the same way human reviewers do: by being right often enough to be worth reading. Measuring the action rate per rule, silencing the noise, and moving the high signal earlier keeps bots pulling their weight.
action rate (findings acted on / reported, 60d):
security/sql-injection 91% keep (PR)
tests/missing-branch 78% keep (PR)
style/line-length 12% -> moved to editor lint
complexity/threshold-10 8% -> raised to 15, scope core
a11y/contrast-auto 6% -> disabled (FP 70%)
ai-reviewer generic nits 14% -> prompt tightened:
only blocking + verified patterns
overall action rate: 31% -> 68%
(feed is now worth reading)The interruption budget is finite
Every bot comment spends a slice of the author’s attention. A rule reporting ten findings a week and getting acted on once is spending fifty interruptions for five fixes. The action rate is the exchange rate between the team’s attention and the bot’s value.
High signal belongs before the PR
A finding that is always right and always fixed is not a review finding — it is a build check with extra steps. Moving it to pre-push hooks or editor squiggles delivers the same correctness with zero interruption, reserving the PR feed for what needs human context.