Ai bug triage

<objective> A hybrid pipeline for bug classification, deduplication, and ticket generation. Deterministic fingerprinting handles deduplication (what LLMs are bad at); LLM handles explanation, severity assessment, and ticket writing (what LLMs are good at).

Key reframe: The LLM is best at explaining and routing, not deduplication. Teach agents to DESIGN the pipeline, not BE the pipeline. </objective>

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Discovery Questions

Check .agents/qa-project-context.md first — it carries tech stack, component mapping, and known flaky areas that improve classification accuracy. Use it and skip anything already answered there. Then clarify:

1. What is the failure source?

   • CI pipeline logs (GitHub Actions, GitLab CI, Jenkins, CircleCI)
   • Test framework output (Playwright, Jest, pytest, Vitest)
   • Production error monitoring (Sentry, Datadog, Bugsnag)
   • Manual bug reports from QA or users

2. What is the ticket destination?

   • Jira, Linear, GitHub Issues, Azure DevOps, Shortcut
   • What fields are required? (component, severity, priority, labels)
   • What workflows exist? (triage board, auto-assignment rules)

3. What is the deduplication scope?

   • Same test run? Same sprint? Same release? All time?
   • Do you already have fingerprinting? What is the current duplicate rate?

4. What approval workflow is needed?

   • Auto-create tickets with human review?
   • Suggest tickets for human approval before creation?
   • Auto-close duplicates? (dangerous -- require approval)

5. What historical data exists?

   • Past bug reports with resolution data?
   • Flaky test history? Known environment issues?
   • Component ownership mapping?

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Core Principles

1. Deterministic first, LLM second. Use stable, reproducible fingerprinting for deduplication and clustering. Use LLM only for tasks requiring understanding: severity classification, root cause hypothesis, and human-readable ticket writing.

2. Normalize before comparing. Raw CI logs are full of timestamps, port numbers, process IDs, and random suffixes that make identical failures look different. Strip all noise before fingerprinting.

3. Fingerprints are anchored to stable elements. Exception type, top stack frames, test name, error message template, and URL pattern are stable. Timestamps, request IDs, and ephemeral ports are not.

4. Human approval before destructive actions. Auto-closing a ticket as duplicate or auto-merging reports requires human confirmation. False deduplication wastes more time than manual triage.

5. Classification drives routing. The value of triage is not the label itself but the routing decision it enables: which team, what priority, what SLA.

6. Track triage accuracy. Measure how often auto-classification matches human judgment. Below 85% accuracy, the pipeline needs tuning.

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The Pipeline

CI Log / Error Report
  │
  ▼
Step 1: NORMALIZE
  Strip timestamps, process IDs, ports, random suffixes, ANSI codes
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Step 2: EXTRACT STABLE ANCHORS
  Exception type, top N stack frames, test name, error message template, URL pattern
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Step 3: HASH CANONICAL FORM
  Deterministic fingerprint from ordered anchors
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  ▼
Step 4: CLUSTER NEAR-DUPLICATES
  Similarity scoring for non-identical but related failures
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  ▼
Step 5: LLM CLASSIFY
  Severity, component, suspected root cause, failure category
  │
  ▼
Step 6: LLM GENERATE TICKET
  Title, description, repro steps, evidence, suggested assignee
  │
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Step 7: HUMAN APPROVAL
  Review before create/close/merge

Step 1: Normalize

Strip noise that makes identical failures look different.

Normalization rules (apply in order):

1. Strip ANSI color codes:        \x1b\[[0-9;]*m → ""
2. Strip timestamps:              \d{4}-\d{2}-\d{2}[T ]\d{2}:\d{2}:\d{2}[.\d]*Z? → "<TIMESTAMP>"
3. Strip UUIDs:                   [0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12} → "<UUID>"
4. Strip process IDs:             pid[=: ]\d+ → "pid=<PID>"
5. Strip port numbers:            :\d{4,5}(?=[\s/,)\]]|$) → ":<PORT>"
6. Strip temp file paths:         /tmp/[^\s]+ → "<TMPPATH>"
7. Strip memory addresses:        0x[0-9a-f]{8,16} → "<ADDR>"
8. Strip random suffixes:         [-_][a-z0-9]{6,8}(?=\.) → "<RAND>"
9. Strip request IDs:             (?:request[_-]?id|trace[_-]?id|correlation[_-]?id)[=: ]["']?[a-zA-Z0-9-]+ → "<REQ_ID>"
10. Collapse whitespace:          \s+ → " "

Example:

Before: 2025-03-22T14:32:01.456Z [pid=42891] Error: Connection refused at 127.0.0.1:54321
        request_id=abc-123-def-456
After:  <TIMESTAMP> [pid=<PID>] Error: Connection refused at 127.0.0.1:<PORT>
        <REQ_ID>

Rule 5 strips the port but not the literal loopback IP — 127.0.0.1 stays in the fingerprint. That's fine for same-host failures, but two runners that bind different hosts (e.g. 127.0.0.1 vs 0.0.0.0) will split into separate fingerprints. If you run heterogeneous hosts, add a rule to normalize bind addresses too.

Step 2: Extract Stable Anchors

From the normalized log, extract elements that identify the failure regardless of environment or timing.

Anchor types (in priority order):

Anchor	Example	Stability
Exception type	TypeError, AssertionError, HTTP 500	Very high
Error message template	Cannot read property 'X' of undefined	High
Top 3 stack frames	at processOrder (order.ts:142)	High
Test name	checkout.spec.ts > completes payment	Very high
URL pattern	POST /api/orders	High
HTTP status code	500, 429, 503	Very high
Exit code	exit code 1, SIGKILL	High
Assertion diff	Expected: 200, Received: 500	Medium

Extraction rules:

• Keep function names but strip line numbers (they change with edits)
• Keep URL paths but strip query parameters and IDs in paths (/api/orders/<ID>)
• Keep error message structure but replace dynamic values with placeholders
• Keep test file and test name exactly as-is

Step 3: Hash Canonical Form

Create a deterministic fingerprint from the extracted anchors.

Algorithm:

1. Sort anchors alphabetically by type
2. Concatenate: exception_type + "|" + message_template + "|" + top_frames + "|" + test_name
3. SHA-256 hash the concatenated string
4. Take first 16 hex characters as fingerprint

Fingerprint properties:

• Same failure always produces same fingerprint (deterministic)
• Different failures produce different fingerprints (collision-resistant)
• Minor log format changes do not change fingerprint (stable)
• Fingerprint is short enough for Jira labels and GitHub tags

Example:

Anchors:
  exception_type: "TypeError"
  message_template: "Cannot read property 'vendorId' of undefined"
  top_frames: "processOrder|groupByVendor|checkout"
  test_name: "checkout.spec.ts > multi-vendor checkout"

Canonical: "TypeError|Cannot read property 'vendorId' of undefined|processOrder|groupByVendor|checkout|checkout.spec.ts > multi-vendor checkout"
Fingerprint: a3f8b2c1e9d04567

Step 4: Cluster Near-Duplicates

Exact fingerprint matching catches identical failures. Similarity scoring catches related failures that differ slightly (same root cause, different manifestation).

Similarity dimensions:

Dimension	Weight	Match Criteria
Exception type	0.30	Exact match
Error message	0.25	Levenshtein distance < 20% of message length
Stack frames	0.25	Jaccard similarity of top 5 frames > 0.6
Component/file	0.10	Same directory or module
Test name	0.10	Same describe block or test file

Clustering threshold: similarity score > 0.75 = likely duplicate, suggest merge.

Human review required for:

• Scores between 0.60 and 0.75 (ambiguous)
• First occurrence of a new fingerprint (no history to compare)
• Failures in components with known intermittent issues

Step 5: LLM Classify

After deterministic fingerprinting and clustering, use the LLM to classify the failure. The prompt feeds in exception, message, top 5 stack frames, test name, and CI context, and asks for five fields:

1. Failure category — test bug | application bug | environment issue | flaky test | build failure
2. Severity — critical | major | minor | trivial (see the severity matrix below)
3. Component — inferred from stack trace and file paths
4. Suspected root cause — 1-2 sentence hypothesis
5. Confidence — high | medium | low; when low, the LLM states what extra information would resolve it

Route low-confidence classifications to human review rather than auto-acting. See references/pipeline-prompts-and-integration.md for the full prompt text and references/classification-taxonomy.md for the bug-category, severity, and component-mapping definitions the prompt should reference.

Failure categories (see references/ci-failure-analysis.md for detail):

Category	Description	Typical Action
Application bug	The app is broken	File bug ticket, assign to owning team
Test bug	The test is wrong	Fix the test, no app change needed
Environment issue	CI infra / network / service down	Retry, notify infra team
Flaky test	Intermittent, non-deterministic	Quarantine, investigate root cause
Build failure	Compilation, dependency, config	Fix build, usually blocking

Step 6: LLM Generate Ticket

Once classified, use the LLM to generate a human-quality bug ticket. The prompt takes the classification plus the normalized error, a log excerpt, and related cluster fingerprints, and produces:

• Title — concise, searchable, includes component name (under 80 chars)
• Description — what happened, in plain language (never raw logs)
• Steps to reproduce — derived from the test name and log context
• Evidence — relevant log lines, assertion diffs, screenshots if available
• Suggested labels — [component, severity, failure-category, fingerprint]
• Suggested assignee — based on component ownership, if known

The fingerprint belongs on the ticket (label and Fingerprint field) so future dedup can match. See references/pipeline-prompts-and-integration.md for the full prompt and the bug report template.

Step 7: Human Approval

No automated action without review. The pipeline suggests; humans decide.

Approval decisions:

• Create ticket — New failure, clear root cause, assign to team
• Merge into existing — Duplicate of known issue, add evidence to existing ticket
• Quarantine test — Flaky test, not an app bug, quarantine and schedule investigation
• Retry and monitor — Environment issue, retry CI, alert if persists
• Dismiss — Known issue already fixed in pending deploy, or test bug with obvious fix

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Severity/Priority Matrix

Severity measures impact. Priority measures urgency. They are independent dimensions.

Severity Definitions

Severity	Definition	Examples
Critical	System unusable, data loss, security breach, no workaround	Payment processing fails, user data exposed, app crashes on launch
Major	Core feature broken, degraded experience, workaround exists	Search returns wrong results, checkout requires page reload, form data lost on back-button
Minor	Non-core feature affected, cosmetic with functional impact	Sorting does not persist, tooltip clipped on mobile, secondary action fails
Trivial	Cosmetic only, no functional impact	Typo in label, 1px alignment, inconsistent capitalization

Priority Definitions

Priority	Definition	SLA (example)
P0	Fix immediately, blocks release or production	Same day
P1	Fix this sprint, significant user impact	This sprint
P2	Fix next sprint, moderate impact	Next sprint
P3	Fix when convenient, low impact	Backlog

Severity x Priority Decision Guide

	Critical	Major	Minor	Trivial
Affects all users	P0	P0	P1	P2
Affects segment (>10%)	P0	P1	P2	P3
Affects few users (<10%)	P1	P1	P2	P3
Edge case only	P1	P2	P3	P3

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Bug Report Template

Use the same template for any bug report, whether auto-generated or human-written. It carries the defect heading, severity/priority/component/environment/fingerprint/reporter metadata, then Description, Steps to Reproduce, Expected/Actual Behavior, Evidence, Frequency, Suggested Root Cause, and Related Issues. See references/pipeline-prompts-and-integration.md for the full copy-paste Markdown template.

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Deduplication Patterns

Pattern	Detection	Action
Exact duplicate	Same fingerprint	Merge into existing ticket, add evidence
Near-duplicate	Same cluster (similarity > 0.75)	Link tickets, suggest merge for human review
Same root cause, different symptom	Same exception type + overlapping frames in different tests	Create parent ticket linking symptom tickets
Regression of fixed bug	Fingerprint matches closed ticket	Reopen ticket, flag as regression, increase priority
Flaky recurrence	Same fingerprint intermittently across CI runs	Tag as flaky, quarantine if rate > 10%

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CI Failure Analysis

See references/ci-failure-analysis.md for comprehensive patterns. Key decision: consistent failure = test bug or app bug; intermittent failure = flaky test or environment; multiple failures at once = environment or shared component; build failure = code or dependency issue.

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Integration Patterns

The pipeline output is tracker-agnostic: Step 6 produces title, description, labels, severity, and component that map to any tracker's fields. See references/pipeline-prompts-and-integration.md for the gh issue create / fingerprint-dedup commands, the GitHub Actions "triage on failure" workflow, and notes on Jira/Linear/Azure DevOps REST/GraphQL integration.

Buy vs Build

Before implementing the full pipeline, check whether a hosted platform already covers the work you'd be doing. Several tools now ship AI-driven test triage that overlaps directly with Steps 4-6.

Platform	Covers	Notes
Trunk Flaky Tests	Fingerprinting, clustering, severity routing, native PR comments + webhooks	Dedicated Agents feature for triage; documented Quarantining workflow — the closest off-the-shelf analog to this skill's pipeline
CloudBees Smart Tests	Fingerprinting, ML-based prioritization, Test Impact Analysis	Formerly Launchable — agents searching old docs may find the old name
Datadog Test Optimization	Flaky Test Management (Auto Retries, Early Flake Detection, Failed Test Replay), Test Impact Analysis	Bits AI Dev Agent now auto-generates fix PRs and Flaky Test Policies auto-quarantine-then-disable after 30 days; pairs with Datadog APM if you're already on Datadog
Sealights	Quality intelligence and test-impact gating	Enterprise; strongest in regulated industries

Use the in-skill pipeline when (a) you need on-prem or air-gapped deployment, (b) your tracker integration is exotic, or (c) you want an explicit AI-prompt audit trail for compliance. Otherwise, buying is usually cheaper than rebuilding fingerprinting + clustering.

Model selection cost note

Use Sonnet 4.6 / Haiku 4.5 for classification (Step 5) — it's cheap and capable enough. Escalate to Opus 4.8 only when the cluster is novel, the failure is ambiguous, or the suggested root cause has low confidence. Burning Opus on every triage is wasteful.

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Anti-Patterns

1. Using LLM for Deduplication

LLMs are non-deterministic. The same two errors compared twice may get different similarity scores. Use deterministic fingerprinting for deduplication; use LLM only for explaining and classifying.

2. Auto-Closing Without Review

Automatically closing a ticket as "duplicate" based on fingerprint matching can merge distinct issues. Always require human confirmation for close/merge actions.

3. Over-Classifying Severity

If everything is "critical," nothing is. Follow the severity matrix strictly. A cosmetic typo is trivial even if it annoys someone.

4. Ignoring Environment Failures

Labeling all failures as "app bug" when many are CI infrastructure issues (Docker OOM, network timeout, disk full). Classify environment issues separately -- they need different remediation.

5. No Feedback Loop

Building the pipeline once and never measuring accuracy. Track: auto-classification accuracy, false duplicate rate, ticket quality ratings from developers.

6. Raw Logs in Tickets

Pasting 500 lines of raw CI output into a bug ticket. Normalize, extract relevant lines, and present the 5-10 lines that matter.

7. Fingerprinting Without Normalization

Hashing raw log lines produces unstable fingerprints that change every run — the same failure gets a different fingerprint each time, so dedup never fires. Normalization is mandatory: always normalize before fingerprinting (Step 1), never fingerprint raw logs.

8. No Component Ownership Mapping

Classification without routing is useless. Maintain a component-to-team mapping so that classified bugs reach the right people.

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Verification

The fingerprinter is the load-bearing piece — prove it before trusting any dedup. Run the 5 stability assertions from references/ci-failure-analysis.md against your implementation:

1. Same error, different timestamps → same fingerprint.
2. Same error, different PIDs and ports → same fingerprint.
3. Same error, different line numbers (code edited) → same fingerprint.
4. Different errors (e.g. TypeError vs RangeError) → different fingerprints.
5. Same exception type, different message property ('name' vs 'email') → different fingerprints.

Tests 1-3 must collapse to one hash; tests 4-5 must produce distinct hashes. A failure here means normalization (Step 1) is leaking noise into the hash or stripping a stable anchor — fix that before tuning clustering. The clustering weights (0.30 / 0.25 / 0.25 / 0.10 / 0.10) must sum to 1.00.

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Done When

• Every failure in failures.json has non-null severity, component, and category fields.
• The 5 fingerprint stability assertions above pass (tests 1-3 same hash, tests 4-5 distinct).
• Duplicates are merged or linked, each pointing to the canonical ticket's fingerprint label.
• Every P0/P1 ticket has an assignee set.
• Auto-classification accuracy is measured and recorded (target > 85%; see qa-metrics).

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Related Skills

• qa-metrics — Track triage accuracy, duplicate rates, mean time to classification, and defect escape rates.
• ci-cd-integration — Pipeline configuration for running triage on test failures, parallel execution, and reporting.
• test-reliability — Runtime per-test healing and quarantine for a single flaky locator. Triage classifies failures; test-reliability fixes one test live.
• observability-driven-testing — Goes the other direction: turns production telemetry into new test designs. Use it when prod errors should spawn tests, not tickets.
• qa-project-context — Project context that improves classification accuracy: component map, known issues, ownership.
• ai-test-generation — Generate regression tests from triaged bug reports.

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Reference Files (in references/)

• pipeline-prompts-and-integration.md — LLM classification + ticket-generation prompts (Steps 5-6), the full bug report Markdown template, and tracker integration code (GitHub Issues, CI workflow, Jira/Linear notes).
• classification-taxonomy.md — Bug categories, severity definitions, component mapping rules, and root cause categories.
• ci-failure-analysis.md — CI log parsing patterns, failure category decision tree, fingerprinting algorithm detail.