🐞 Bug Investigator Agent Specification¶

🧠 Purpose & Position in QA Ecosystem¶

The Bug Investigator Agent is the AI Software Factory’s autonomous failure diagnostics and root cause analysis engine. It is responsible for:

Analyzing failed tests, crashes, and regressions
Determining why the failure happened
Classifying bugs (test bug vs. product bug vs. infra issue)
Fingerprinting and tracking regressions over time
Suggesting automated or human remediation strategies

While the QA Engineer Agent determines if a build can pass QA, the Bug Investigator Agent determines why it might be failing — and what can be done about it.

🧭 Strategic Role in the QA Ecosystem¶

Role	Description
🕵️‍♂️ Triage Agent	Investigates failing tests, regressions, trace errors
🔁 Regression Tracker	Compares bug symptoms across builds, editions, and modules
🧪 Flakiness Detector	Identifies and classifies unstable test cases
🧠 Memory-Backed Bug Identifier	Matches new failures to known bugs using embeddings or hashes
🧑‍💻 Developer Support Node	Escalates unresolved issues to Studio or HumanOps for triage
🧩 Collaborator Agent	Works with QA Engineer, Test Generator, and CI/CD agents to close the loop

🔁 Bug Lifecycle Flow Position¶

flowchart TD
    QA[QA Engineer Agent] -->|Regression, Failures| Bug[🐞 Bug Investigator Agent]
    Bug -->|Diagnosis| Studio[Studio Dashboard]
    Bug -->|Fix Recommendation| Dev[Developer]
    Bug -->|Flakiness Detected| Test[Test Generator Agent]
    Bug -->|False Positive| QA

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🧱 What the Bug Investigator Agent Guarantees¶

Guarantee	Description
All failures are investigated	No failing test is accepted blindly — root cause required
False positives are isolated	Prevents CI/CD pipeline noise from flaky or non-code-related errors
Recurrent bugs are recognized	Matches regressions against historical memory
Clear outputs are emitted	Generates traceable JSON and YAML-based diagnostics, human-readable markdown summaries
Code + test are both considered	Determines whether to patch test, retry, or suggest code fix

👥 Agents It Collaborates With¶

Agent	Reason
QA Engineer Agent	Receives failed tests and regression reports
Test Generator Agent	Sends test mutation suggestions and retry patterns
HumanOps Agent	Escalates hard-to-automate bug reports
Studio Agent	Publishes diagnosis + known issue badges
Code Reviewer Agent	Proposes code annotations for suspicious modules
CI/CD Agent	Feeds back status change (`fail → retry`, `fail → override`)

🎯 Strategic Value to ConnectSoft¶

The Bug Investigator Agent enables:

📉 Lower false-positive test rates
🔁 Faster regression detection
🧪 More stable test pipelines
🧠 Persistent memory of quality risk hotspots
🔎 AI-assisted debugging without human intervention in most cases

✅ Summary¶

The Bug Investigator Agent is the AI QA detective — trained to autonomously:

Analyze failures
Find root causes
Detect flaky or unstable tests
Suggest remediations
Track and cluster regressions across time and editions

It ensures deep QA diagnostics at scale, supporting ConnectSoft’s goal of AI-driven test stability, software resilience, and autonomous software debugging.

🧭 Core Responsibilities¶

Responsibility	Description
🧠 Root Cause Analysis	Diagnose why a test failed (code bug, test bug, infra issue, timing, config)
🔄 Regression Identification	Determine if the issue is a known regression or a newly introduced bug
🔁 Failure Deduplication	Group failures with the same root cause or crash signature
🔎 Flaky Test Detection	Detect non-deterministic tests by analyzing historical runs and failure patterns
🛠 Fix Recommendation	Propose automated or human-involved actions (retry, timeout, patch, escalate)
🧩 Failure Classification	Tag bug as code issue, infra flake, test logic bug, config problem, edition-specific error
📚 Bug Fingerprinting	Generate a hashable signature to cluster and track similar bugs across builds and tenants
📥 Escalation Triage	Escalate unresolvable or high-impact bugs to Studio or HumanOps Agent with markdown summary
🧾 Bug Artifact Generation	Emit structured artifacts: `bug-fingerprint.json`, `flaky-tests-index.yaml`, `fix-recommendation.yaml`
🧠 Memory Updates	Persist known regressions, bug fix states, and historical patterns to improve future triage speed

📂 Bug Investigation Artifact Catalog¶

Artifact	Description
`bug-fingerprint.json`	Unique, hashable description of the failure cause
`regression-cluster.yaml`	Aggregated bugs traced to the same issue
`flaky-tests-index.yaml`	Flagged unstable test cases with metadata
`fix-recommendation.yaml`	Test or code fix proposals (retry, adjust, refactor, ignore)
`diagnostic-summary.md`	Human-readable explanation of the failure and suggested next steps
`false-positive-log.json`	Tracks known harmless failures, supports auto-pass logic if policy permits

📘 Example: `fix-recommendation.yaml`¶

testId: AppointmentCancelTest
diagnosis: UI flake caused by delayed modal rendering
recommendation:
  action: retryWithDelay
  delayMs: 1000
  reasoning: Render delay detected in span trace; test retry advised
confidence: 0.93

🧩 Decision-Making Modes¶

Mode	Trigger
🔁 Retry Suggestion	Intermittent failures with stable root
🧪 Test Patch Suggestion	Invalid assertion, missing waitFor, UI race
🧑‍💻 Code Fix Suggestion	Stack trace or state mismatch rooted in app logic
❌ Infra Issue	Test runner crash, environment timeout, external service dependency
⚠️ Unknown / Escalate	No pattern match, high impact, requires human analysis

🤖 Output Consumers¶

Agent / Tool	Consumes
QA Engineer Agent	Regression classification, flakiness index
Studio Agent	Badge display, debug info view, known issue map
Test Generator Agent	Input for stabilizing or mutating failing test cases
HumanOps Agent	Triage summaries requiring developer intervention
CI/CD Agent	Rerun or retry rules for pipelines with flakes or transient bugs

✅ Summary¶

The Bug Investigator Agent is responsible for:

🕵️‍♂️ Diagnosing and classifying every failure
🔁 Mapping bugs to fingerprints and known patterns
🧪 Detecting flakiness with statistical memory
🛠 Recommending next actions (retry, patch, escalate)
📤 Emitting structured bug artifacts for use across the QA ecosystem

It plays a critical cross-cutting role in ConnectSoft’s quality model, ensuring failures are explainable, traceable, and fixable.

📥 Inputs Consumed¶

This section defines the full set of structured, semi-structured, and contextual inputs that the Bug Investigator Agent ingests to diagnose, classify, and resolve failures in the ConnectSoft Software Factory.

These inputs originate from test execution, observability systems, source control metadata, and QA status artifacts.

📂 Primary Input Artifacts¶

Input File	Description
`test-results.json`	Full test results from Test Automation Agent, including pass/fail, assertions, logs
`qa-summary.json`	QA verdict with associated failing test IDs and scoring data
`regression-matrix.json`	List of new or repeated regressions detected by QA Engineer Agent
`trace-logs.json`	Telemetry spans and OpenTelemetry error signals from Observability Agent
`unhandled-exceptions.json`	Raw stack traces, crash metadata (mobile, backend, web)
`test-gap-report.yaml`	Uncovered or unstable test areas — used to correlate drift or root cause distance
`flaky-tests-index.yaml`	Previously identified unstable tests
`build-manifest.json`	Modules, commits, and components changed in current build
`edition-config.yaml`	Edition/tenant rules, enabled/disabled features, screens to consider
`studio.qa.annotations.json`	Optional human or agentic feedback from prior failures (notes, tags)

🧠 Inferred Inputs (via Kernel Memory or Event Graph)¶

Inferred Input	Description
`pastRegressionHistory[]`	Similar failure signatures from prior builds
`testExecutionFlakinessScore`	Based on N-run history of test ID
`componentUnderTest`	Deduced from failure trace, affected file path, screen ID
`editionIsolationHint`	Indicates edition-scoped issue (e.g. failure only in `vetclinic-premium`)
`blameCandidates[]`	Functions, modules, or code authors linked to the error path
`knownBugSimilarityIndex`	Embedding-based similarity match to known bugs in vector DB

📘 Sample: `test-results.json` (subset)¶

{
  "testId": "CancelAppointmentWithModal",
  "status": "fail",
  "error": "Expected element not visible",
  "stackTrace": "ModalDialog.tsx: open() → render → timeout",
  "retryCount": 0,
  "durationMs": 5342,
  "platform": "flutter",
  "editionId": "vetclinic-blue"
}

📘 Sample: `unhandled-exceptions.json`¶

[
  {
    "errorType": "NullReferenceException",
    "location": "AppointmentService.cs: Line 88",
    "traceId": "trace-829fa",
    "screen": "AppointmentScreen",
    "platform": "maui",
    "edition": "vetclinic-premium"
  }
]

🧩 Optional Runtime Hints (Advanced Inputs)¶

Hint	Purpose
`previousPassInLastNBuilds`	Used to calculate flakiness threshold
`testWasRecentlyUpdated`	Suggests potential local cause vs unrelated system issue
`crashInUnhandledScreen`	Indicates a gap not triggered by test logic
`API contract drift`	Suggests if a schema mismatch caused failure

🔄 Input Types Summary¶

Input Type	Source	Frequency
Structured artifacts (JSON/YAML)	Other agents	Per build
Observability traces	Live span/log exports	On error
Vector similarity input	Memory layer	On regression
Human annotations	Studio / QA review	On escalation

✅ Summary¶

The Bug Investigator Agent consumes:

📁 Test failures
🔥 Stack traces and telemetry logs
🧪 Regression summaries and flakiness scores
🔄 Build context, code diff, edition features
🧠 Semantic history and known bug memory

These inputs enable the agent to deliver precise, explainable, and context-rich root cause analysis — powering autonomous diagnostics at scale.

📤 Outputs Produced¶

This section defines the structured outputs the Bug Investigator Agent emits after analyzing regressions, crashes, and flaky tests. These outputs are shared with QA, CI/CD, Test Generator, Studio, and optionally HumanOps — closing the diagnostics loop across ConnectSoft’s autonomous factory.

📦 Core Output Artifacts¶

File	Purpose
`bug-fingerprint.json`	Canonical fingerprint of the failure cause — hashable and traceable
`fix-recommendation.yaml`	Suggests code/test/config fix or retry logic with justification
`regression-cluster.yaml`	Groups related failures/regressions into a shared root cause
`flaky-tests-index.yaml`	Updated list of unstable/flaky tests with supporting evidence
`diagnostic-summary.md`	Human-readable explanation, symptoms, blame, and recommended next steps
`false-positive-log.json`	Known false positives (e.g. infra issue, UI race) flagged for override by policy
`debug-handoff.md`	Escalation payload routed to HumanOps or Studio when investigation is inconclusive
`studio.qa.bug.status.json`	Dashboard-friendly QA verdicts and bug status metadata

📘 Example: `bug-fingerprint.json`¶

{
  "fingerprintId": "bug-7f2c9d45",
  "summary": "Modal fails to open during CancelAppointmentFlow",
  "module": "ModalDialog.tsx",
  "trigger": "UI render timeout",
  "platform": "flutter",
  "editionId": "vetclinic-blue",
  "hash": "c7a9c1d8e7e42941",
  "confidence": 0.92
}

📘 Example: `fix-recommendation.yaml`¶

fingerprintId: bug-7f2c9d45
recommendation:
  action: increaseWait
  delayMs: 1000
  justification: Modal element visible in span after ~850ms; default wait 500ms insufficient
  confidence: 0.91
appliesTo:
  testId: CancelAppointmentWithModal
  platform: flutter
  edition: vetclinic-blue

📘 Example: `diagnostic-summary.md`¶

## 🐞 Bug Report — CancelAppointmentWithModal

- **Status**: Flaky Test (UI Race Condition)
- **Trigger**: Modal not rendered within expected window
- **Affected Module**: ModalDialog.tsx → open()
- **Edition**: vetclinic-blue
- **Test ID**: CancelAppointmentWithModal

### Suggested Fix
Increase modal wait threshold by 500ms OR use `waitForVisible()` utility wrapper.

> Bug Fingerprint: bug-7f2c9d45 • Confidence: 91%

🎯 Output Consumers¶

Agent	Consumes
QA Engineer Agent	Integrates `regression-cluster.yaml` and `flaky-tests-index.yaml` into scoring
Test Generator Agent	Uses `fix-recommendation.yaml` to regenerate or mutate failing tests
CI/CD Agent	Honors `false-positive-log.json` for retry or bypass logic
Studio Agent	Displays `studio.qa.bug.status.json` in test explorer and build overview
HumanOps Agent	Receives `debug-handoff.md` for manual triage if required

📎 Trace Tags in Outputs¶

All artifacts include:

traceId
testId
platform
editionId
fingerprintId
confidenceScore
generatedAt

✅ Summary¶

The Bug Investigator Agent produces:

📑 Structured, machine-readable root cause reports
🛠 Actionable fix suggestions for test or code
🔁 Clustered regression metadata
🧪 Flaky test index for pipeline resilience
🧑‍💻 Markdown summaries for developers and QA leads

These outputs form the diagnostic layer of the Software Factory, enabling explainable AI debugging, faster resolution, and smarter automation.

🔄 Execution Flow¶

This section outlines the step-by-step flow followed by the Bug Investigator Agent — from initial failure detection to root cause analysis and remediation recommendation. The flow is modular, traceable, and memory-augmented to support fast and scalable diagnostics.

🧭 High-Level Process Flow¶

flowchart TD
    START[🔔 Receive Failure or Regression Trigger]
    LOAD[📥 Load Artifacts and Logs]
    CLASSIFY[🧪 Classify Failure Type]
    FINGERPRINT[🧠 Generate Bug Fingerprint]
    MATCH[🔍 Match Against Known Issues]
    ANALYZE[🧠 Deep Dive: Stack Trace + Span + Module Diff]
    DIAGNOSE[✅ Determine Root Cause]
    SUGGEST[💡 Recommend Fix or Retry Strategy]
    OUTPUT[📤 Emit Artifacts and Summary]
    ESCALATE{Confidence < Threshold or Ambiguous?}
    MANUAL[🧑‍💻 Emit Human Review Handoff]
    DONE[🏁 Finish]

    START --> LOAD --> CLASSIFY --> FINGERPRINT --> MATCH --> ANALYZE --> DIAGNOSE --> SUGGEST --> OUTPUT --> ESCALATE
    ESCALATE -- No --> DONE
    ESCALATE -- Yes --> MANUAL --> DONE

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🪜 Execution Phase Details¶

Phase	Description
1. Receive Trigger	Reacts to test failures, unhandled exceptions, regressions, or crash reports
2. Load Artifacts	Loads `test-results.json`, `stack traces`, `span logs`, and build metadata
3. Classify Failure Type	Tags the failure as flaky test, product bug, infra issue, config error, or unknown
4. Fingerprint	Creates a unique hash of the bug (stack trace, test ID, module, edition)
5. Match Known Bugs	Uses similarity search or hash match to find related past regressions
6. Deep Dive Analysis	Examines logs, module diffs, retries, edition context, observability metadata
7. Diagnose Root Cause	Determines high-confidence reason (e.g. modal timeout, API error, data race)
8. Suggest Fix	Outputs action: test retry, delay, assertion patch, code fix suggestion
9. Output Artifacts	Emits JSON/YAML + Markdown summaries
10. Escalate if Needed	Routes low-confidence or ambiguous bugs to HumanOps Agent

🧠 Execution Behavior by Trigger¶

Trigger Type	Behavior
Regression from QA Agent	Compare to past fingerprint, update matrix, reclassify
Crash Log (Observability)	Trace span to test, correlate with failure or gap
Test Failure	Retry analysis (e.g., recent updates, unstable test match)
Unhandled Screen Exception	Screen-path inference → find likely test gaps

📘 Sample Internal State Snapshot¶

{
  "testId": "CancelAppointmentModal",
  "traceId": "proj-814-v2",
  "classification": "Flaky Test - UI Race",
  "fingerprintId": "bug-7f2c9d45",
  "match": {
    "type": "approximate",
    "confidence": 0.89
  },
  "suggestedFix": "increaseWait(1000ms)",
  "escalated": false
}

🧑‍💻 Escalation Path¶

Escalation Trigger	Result
`confidence < 0.75`	Output `debug-handoff.md`
`trace mismatch or undefined root`	Flag for human triage
`repeat unexplained failures`	Sent to Studio QA review panel
`edition-exclusive failures`	Escalate with edition override metadata

🧾 Key Features of the Flow¶

🧠 Uses bug memory and history to improve accuracy
🔁 Identifies both repeat regressions and first-time failures
🎯 Focuses on cause and resolution — not just logging the problem
🛠 Links output directly to test retry, patch, or refactor decisions

✅ Summary¶

The Bug Investigator Agent follows a deterministic, intelligent execution flow to:

Detect and classify failures
Link them to known patterns
Diagnose root causes
Suggest recoveries or fixes
Escalate only when automation is insufficient

This enables scalable, explainable bug diagnostics, completing the feedback loop within ConnectSoft’s autonomous QA pipeline.

🧩 Semantic Kernel Skills¶

This section lists and describes the Semantic Kernel skills that power the Bug Investigator Agent’s behavior. Each skill is focused, composable, and reusable, allowing the agent to execute failure diagnostics, classification, regression memory matching, and fix recommendation workflows.

🧠 Core Semantic Kernel Skills¶

Skill	Purpose
`ClassifyFailureTypeSkill`	Categorizes the root failure: code, test logic, infra, config, unknown
`GenerateBugFingerprintSkill`	Creates a canonical signature based on test ID, error, stack, trace
`MatchToKnownBugsSkill`	Searches vector DB or hash index for similar or known issues
`AnalyzeCrashTraceSkill`	Parses unhandled exceptions, telemetry logs, and stack frames
`DetermineFlakinessScoreSkill`	Analyzes test history for instability or inconsistency
`SuggestFixActionSkill`	Proposes remediation: retry, test patch, code diff, or escalation
`GenerateBugArtifactsSkill`	Emits `bug-fingerprint.json`, `fix-recommendation.yaml`, etc.
`UpdateBugMemorySkill`	Stores fingerprint, match result, and diagnostics in persistent memory
`EmitEscalationSummarySkill`	Creates `diagnostic-summary.md` or `debug-handoff.md` for human review
`ClusterRegressionsSkill`	Groups regressions into shared clusters by module/symptom/root cause
`TraceToTestMapSkill`	Links observability logs to test IDs and screens using route/screen info

📘 Skill Composition Example¶

When the agent receives a failed test:

1. → `ClassifyFailureTypeSkill`  
2. → `GenerateBugFingerprintSkill`  
3. → `MatchToKnownBugsSkill`  
4. → `AnalyzeCrashTraceSkill`  
5. → `SuggestFixActionSkill`  
6. → `GenerateBugArtifactsSkill`
7. → If confidence < 0.75 → `EmitEscalationSummarySkill`

📘 Sample Skill Output – `SuggestFixActionSkill`¶

{
  "testId": "CancelAppointmentModal",
  "fingerprintId": "bug-7f2c9d45",
  "recommendation": {
    "action": "increaseWait",
    "reason": "UI modal appears after 800ms; test timeout was 500ms",
    "delayMs": 1000
  },
  "confidence": 0.91
}

🧩 Reusable Skill Integration¶

Used In	Reuses Skills
QA Engineer Agent	`DetectRegressionSkill`, `UpdateBugMemorySkill`
Test Generator Agent	`SuggestFixActionSkill`, `FlakyScoreSkill`
HumanOps Agent	`EmitEscalationSummarySkill`
Studio Agent	`GenerateBugArtifactsSkill`, `ClusterRegressionsSkill`

🔄 Skill Execution with Context¶

All skills are executed with full trace context:

traceId, testId, platform, editionId
stackTrace, errorMessage, logs, test history
Memory embeddings from known-regressions-index or bug-fingerprint-DB

✅ Summary¶

The Bug Investigator Agent is powered by a suite of purpose-specific Semantic Kernel skills that allow it to:

Classify and diagnose bugs
Generate traceable fingerprints
Suggest corrective actions
Share structured outputs with other agents
Improve continuously using memory and past bug history

These skills make the Bug Investigator a modular, explainable, and extensible diagnostic engine in the ConnectSoft Software Factory.

⚙️ Failure Type Classification¶

This section defines the taxonomy of failure types used by the Bug Investigator Agent to classify failures. Classification helps:

Determine if the bug is a true code issue, infrastructure flake, or test design flaw
Suggest the correct next step (retry, fix, escalation)
Annotate bug fingerprints for QA and CI/CD agents

🧩 Primary Failure Categories¶

Category	Description	Example
🧪 Test Logic Bug	Failure is caused by an incorrect or brittle test	Test asserts too early before UI element is visible
💥 Application Code Bug	Legitimate defect in business logic, API, UI, etc.	NullReferenceException in `AppointmentService.cs`
⚠️ Flaky/Unstable Test	Test fails intermittently due to timing, async, race conditions	Modal doesn’t render fast enough 2/10 runs
🛠️ Infrastructure Failure	CI runner crash, network timeout, build failure unrelated to code	"Could not connect to WebDriver"
🔐 Config/Edition Mismatch	Feature disabled in one edition but test assumes it’s present	B2C screen tested on B2B edition
🔎 Unknown/Undiagnosed	Error is unclassifiable or incomplete, requires escalation	Unstructured log dump with no test trace match

📘 Classification Output Example¶

{
  "testId": "CancelAppointmentModal",
  "classification": "Flaky Test",
  "subtype": "UI render timing",
  "confidence": 0.91,
  "reason": "Failure occurs intermittently; element visible after 850ms; test timeout 500ms",
  "rootCause": "ModalDialog.tsx → render()"
}

🧠 Classification Criteria (by Skill)¶

Input Signal	Used By	Indicates
`test failure history`	`DetermineFlakinessScoreSkill`	Flaky or stable
`stack trace path`	`ClassifyFailureTypeSkill`	Code bug vs infra
`error pattern`	Regex + vector search	Match to known classification
`testId + edition mismatch`	Rule-based check	Edition-config conflict
`retry success`	Execution result	Confirms flake or instability

🧑‍💻 Developer View (Studio or PR Summary)¶

### 🐞 Failure Classification

- **Type**: Flaky Test  
- **Subtype**: UI race condition  
- **Confidence**: 91%  
- **Suggested Action**: Increase wait to 1000ms or use waitFor utility  
- **Edition Impact**: vetclinic-blue only  
- **Module**: ModalDialog.tsx

🔄 Classification Impact on Pipeline¶

Classification	Result
Test Bug	Retry or patch suggested, test flagged
Code Bug	Escalation to QA / HumanOps, blocks build
Flaky Test	Retry allowed, QA score reduced
Infra Issue	Retry or ignore (per config)
Edition Mismatch	Route to Edition Coordinator + Test Generator
Unknown	Escalate to HumanOps with `debug-handoff.md`

📎 Classification Tags in Artifacts¶

Field	Example
`classification`	`"Flaky Test"`
`subtype`	`"UI render delay"`
`rootCause`	`"ModalDialog.tsx: open() method"`
`confidenceScore`	`0.91`
`editionContext`	`"vetclinic-blue"`

✅ Summary¶

The Bug Investigator Agent classifies each failure into a precise category to determine the appropriate resolution path:

✅ Test bug → suggest patch
✅ Code bug → escalate and block
✅ Flake → retry or stabilize
✅ Config error → route to edition/test agents
❌ Unknown → emit detailed debug summary

This allows deterministic and scalable QA diagnostics with traceable root cause attribution.

💥 Crash Analysis & Log Inference¶

This section defines how the Bug Investigator Agent performs crash diagnostics and log parsing to:

Identify unhandled exceptions, runtime crashes, or telemetry anomalies
Map these errors to relevant tests, modules, and code paths
Support root cause analysis even in untested or undetected flows

🧩 Crash & Log Inputs¶

Input	Description
`unhandled-exceptions.json`	Raw exception traces from runtime environments (mobile, backend, web)
`trace-logs.json`	OpenTelemetry spans + error traces
`application-logs.txt`	(Optional) Aggregated logs from the failing session or environment
`stackTrace` (from test-results)	Test-level error location metadata

🧠 Crash Parsing & Pattern Matching¶

Stack trace analysis: language-specific parsers extract method, line, module
Similarity matching: against known crash signatures via embeddings
Span-to-test correlation: links failed spans to test IDs or screen routes
TraceId propagation: supports E2E correlation from crash → screen → test

📘 Example: Parsed Exception (`unhandled-exceptions.json`)¶

{
  "errorType": "NullReferenceException",
  "message": "Object reference not set to an instance of an object",
  "stack": [
    "AppointmentService.cs:Line 88",
    "BookingWorkflow.cs:Line 122"
  ],
  "screen": "Appointments",
  "traceId": "trace-9917a1",
  "platform": "maui",
  "edition": "vetclinic-premium"
}

→ Bug Investigator links this crash to the BookAppointmentTest, flags root cause as code bug.

📘 Example: Inferred Crash Bug Output¶

{
  "fingerprintId": "bug-8a12e9fa",
  "classification": "Application Code Bug",
  "rootCause": "Null object at AppointmentService.cs:Line 88",
  "relatedTestId": "BookAppointmentTest",
  "editionId": "vetclinic-premium",
  "confidence": 0.94
}

🔍 Crash Location Attribution¶

Signal	Result
Stack trace → testId	If exact match exists, link directly
Span → route → screen	Infer likely test from screen or navigation path
Function + file hash match	Use blame data to tag test or responsible engineer/module

🔬 Log Analysis Techniques¶

Regex extraction for known error patterns
Log-time clustering (group logs by test timestamp/session)
Correlation to OpenTelemetry exception.event, status_code, and log.message
Timeout/latency detection (duration_ms > threshold) for performance-induced failures

🧑‍💻 Developer-Friendly Debug Summary¶

### 🐞 Runtime Crash — Appointments Module

- **Crash**: NullReferenceException in `AppointmentService.cs:Line 88`
- **Test Affected**: `BookAppointmentTest`
- **Edition**: vetclinic-premium
- **Stack**:
  - AppointmentService.cs:Line 88
  - BookingWorkflow.cs:Line 122
- **Action**: Escalate to HumanOps or refactor null-check logic

🔄 Action Routing from Crash¶

Crash Type	Action
Known issue → existing fingerprint	Cluster and annotate
New, high-confidence bug	Generate `bug-fingerprint.json` + fix recommendation
Untestable crash (no linked test)	Emit to `test-gap-report.yaml`
Ambiguous crash	Emit `debug-handoff.md` to HumanOps

✅ Summary¶

The Bug Investigator Agent uses crash signals to:

Parse and trace unhandled exceptions
Link logs and spans to affected screens/tests
Diagnose code bugs missed by tests
Route suggestions or escalations accordingly
Strengthen QA scoring even on runtime-only failures

This closes the gap between observability and test-driven QA, ensuring crash resilience is always traceable.

🔁 Flaky Test Detection & Tagging¶

This section details how the Bug Investigator Agent identifies, scores, and manages flaky (intermittently failing) tests — one of the most common sources of pipeline instability, false positives, and CI/CD inefficiency.

The agent ensures that test flakiness is detected early, automatically flagged, and routed for stabilization or intelligent retry.

🧪 What Is a Flaky Test?¶

A test that passes sometimes and fails sometimes — without changes to code, config, or environment — due to timing, async behavior, randomness, or external dependency variance.

🧠 Detection Signals¶

Signal	Description
`N-run instability`	Same test passes/fails in >2 of the last 5 builds
`Duration variability`	Test duration fluctuates >50% between runs
`Span-based delay detection`	Logs/telemetry show unstable rendering, loading, or async behavior
`Stack trace inconsistency`	Failures appear in different places in same test
`Retry passes`	Test failed once but passed on retry (e.g., with longer wait)

📘 Example: Flaky Test Score Output¶

{
  "testId": "FeedbackSubmissionTest",
  "classification": "Flaky Test",
  "flakyScore": 0.88,
  "failCount": 3,
  "passCount": 4,
  "averageDurationMs": 5200,
  "durationVariance": 0.53,
  "retrySuccess": true,
  "reason": "UI transition delay on submit button"
}

📘 `flaky-tests-index.yaml`¶

- testId: FeedbackSubmissionTest
  flakyScore: 0.88
  platform: react-native
  module: FeedbackScreen
  classification: UI render timing issue
  recommendation:
    action: add waitFor(button.enabled)
    retriesAllowed: true
  trackedSince: 2025-05-01

🧩 Flakiness Score Formula (Heuristic)¶

score = weighted(unstable history + retry success + duration variance + span delay confidence)

Threshold: score > 0.75 → flagged as flaky

🔁 Retry Handling¶

Policy-Driven Behavior	Action
`flakyScore > threshold` and `retryAllowed: true`	Auto-retry test once or twice
Retry success	Downgrade bug severity, allow pass (if policy allows)
Retry fail	Escalate to `debug-handoff.md` and fail build
Retry not supported	Block until test stabilization or manual review

🧱 Outputs Affected by Flakiness Detection¶

Output File	Purpose
`qa-summary.json`	Confidence score reduced if flaky tests affect coverage or regression analysis
`test-gap-report.yaml`	Lists modules with unstable test reliability
`fix-recommendation.yaml`	Suggests test-level fixes: waitFor, debounce, stabilize data
`studio.qa.status.json`	Flags flaky tests in Studio dashboard tiles
`manual-review-needed.md`	Triggers QA override or triage for critical instability

🧠 Agent Memory¶

Flaky test fingerprints are stored in:

flaky-tests-index.yaml
bug-fingerprint-db
Annotated regressions for historical trend tracking

Flakiness score history is kept per testId and editionId for intelligent rerouting and fix recommendation.

✅ Summary¶

The Bug Investigator Agent:

🧪 Detects flaky tests using historical, runtime, and retry signals
🔁 Tags instability and adjusts QA confidence accordingly
📉 Reduces false positives and prevents noisy pipeline failures
🧠 Maintains memory to suppress redundant triage
🔧 Guides the Test Generator Agent in stabilizing test cases

This helps keep ConnectSoft’s CI/CD pipelines resilient, reliable, and self-healing — at massive scale.

🔁 Regression Fingerprinting & Tracking¶

This section describes how the Bug Investigator Agent fingerprints, clusters, and tracks regressions across builds, editions, and environments. It enables early detection of recurring issues, grouping of failures by root cause, and automated suppression of redundant diagnostics.

🧠 What Is a Regression Fingerprint?¶

A stable, hashable identifier that represents a unique root cause or symptom pattern across test failures, logs, stack traces, and platform/edition combinations.

A fingerprint allows the Bug Investigator Agent to deduplicate failures, track regression families, and inform confidence scoring across the QA ecosystem.

🧩 Fingerprint Sources¶

Source	Description
Stack trace	Top 3–5 frames, method + file + line context
Test ID + screen/module	Namespaced per platform + edition
Span signature	Failing OpenTelemetry span paths
Edition ID	Bugs isolated to certain tenant configurations
Error message	Normalized hash of error text or log key
Code blame hash (optional)	Git diff metadata linked to line/module

📘 Example: `bug-fingerprint.json`¶

{
  "fingerprintId": "bug-a47fb90c",
  "module": "AppointmentService.cs",
  "classification": "Code Bug",
  "errorHash": "dc39e5b2e3",
  "stackHash": "73b2-9cf1-a2a8",
  "editionId": "vetclinic-premium",
  "testId": "BookAppointmentTest",
  "firstSeen": "2025-04-22",
  "lastSeen": "2025-05-15",
  "occurrences": 4,
  "matchConfidence": 0.94
}

📘 Example: `regression-cluster.yaml`¶

fingerprintId: bug-a47fb90c
cluster:
  - booking-v5.2.0
  - booking-v5.2.1
  - booking-v5.3.0
relatedTests:
  - BookAppointmentTest
  - ConfirmAppointmentAnalytics
suggestedAction: escalate

🔁 Fingerprinting Process¶

Normalize stack traces, error messages, and spans
Generate hash and embeddings
Search known-bugs-index for match
If no match → create new fingerprint and cluster
If match → increment occurrence count, reuse history
Update QA scoring, dashboards, and reports

🧠 Bug Memory Storage¶

Layer	Content
`bug-fingerprint-db`	Fingerprint → root cause metadata
`regression-clusters`	Aggregates regressions by cause/module
`flaky-fingerprint-index`	Cross-linked instability scoring
`known-bugs-index.vec`	Vector-based embedding similarity search
`bug-impact-matrix.json`	Test IDs + modules + editions impacted per bug

📎 Outputs That Use Fingerprints¶

Output	Purpose
`qa-summary.json`	Links regressions to known fingerprint IDs
`studio.qa.status.json`	Displays known bug badges and trend lines
`fix-recommendation.yaml`	Uses fingerprint ID for grouped fix suggestions
`debug-handoff.md`	Links to regression history + related trace IDs

📊 Studio Impact View¶

📍 Show recurring bug markers per test or screen
🔁 Group test failures by fingerprint in dashboard
🔄 Trend line: “Seen in 4 of last 5 builds”
🧭 View: “Affects 3 editions: vetclinic-blue, wellness-lite, healthhub-basic”

✅ Summary¶

The Bug Investigator Agent:

🔁 Fingerprints every regression into a reproducible root cause ID
📚 Tracks bugs across builds, editions, and test IDs
🧠 Maintains memory of recurrence, false positives, and known clusters
🔧 Links failures to fix suggestions or escalation triggers
📊 Feeds Studio dashboards and QA scoring with regression intelligence

This provides a high-resolution diagnostic memory, helping the AI Software Factory become self-aware of its defect history and trend patterns.

🎭 Edition-Specific Bug Handling¶

This section details how the Bug Investigator Agent supports edition-aware diagnostics to ensure bugs and regressions are correctly scoped by tenant, region, feature set, or white-labeled configuration.

Edition-scoped bug handling is critical in ConnectSoft’s multi-tenant, customizable SaaS factory — where each edition may have exclusive screens, conditional features, or localized flows.

🎯 Why Edition Scoping Matters¶

Bugs may only manifest in certain edition combinations (e.g. dark theme, disabled modules)
Some regressions are false positives outside a specific edition
QA coverage varies per edition — root causes must respect edition test maps
The same screen or test may behave differently due to edition-based config

📘 Inputs Used for Edition Context¶

Input File	Role
`edition-config.yaml`	Declares active features, modules, branding, locale
`test-results.json`	Annotated with `editionId`, `platform`, `tenantId`
`qa-summary.json`	May include edition violations or missing coverage
`stack traces + span traces`	Often tagged with `traceId` + edition context
`test-gap-report.yaml`	Lists untested edition-specific modules or screens

🧩 Example: `edition-config.yaml`¶

editionId: vetclinic-premium
features:
  enableChat: true
  enableAppointments: true
screens:
  include: [LoginScreen, Appointments, Profile]
  exclude: [MarketingConsentScreen]

📘 Bug Fingerprint with Edition Tag¶

{
  "fingerprintId": "bug-92d14f71",
  "testId": "CancelAppointmentTest",
  "module": "AppointmentsScreen",
  "classification": "Flaky Test",
  "editionId": "vetclinic-premium",
  "platform": "flutter",
  "matchConfidence": 0.89
}

→ This ensures the same test failing in vetclinic-lite may not be treated as a regression if that screen doesn't exist.

🔄 Edition-Aware Clustering Rules¶

Scenario	Behavior
❗ Bug occurs only in 1 edition	Fingerprint ID is edition-bound
✅ Bug occurs across editions	Group into global cluster
⛔ Feature not enabled in edition	Do not classify as regression or real test
🔁 Test result in edition mismatch	Flag in `edition-test-violation.yaml`

📊 Studio View Impact¶

Feature	Description
Bug markers show edition badge	Example: “Bug affects vetclinic-blue only”
Toggle filters by edition/tenant	QA can filter bugs by scope
Tooltip shows test IDs, editions, and trace counts per bug

📘 Sample: `edition-test-violation.yaml`¶

violations:
  - testId: ChatScreenToggleTest
    runOnEdition: vetclinic-lite
    issue: Feature not enabled in this edition
    action: skip or adjust test scope

📦 Outputs Supporting Edition Context¶

File	Purpose
`bug-fingerprint.json`	Contains `editionId`, `platform`, and testId
`regression-cluster.yaml`	Aggregates by edition if needed
`debug-handoff.md`	States edition context if escalation is required
`studio.qa.bug.status.json`	Feeds edition-scoped dashboard views

✅ Summary¶

The Bug Investigator Agent supports precise edition-based QA diagnostics:

🧭 Tracks bugs by editionId, tenantId, and feature scope
🧩 Prevents false regression flags in excluded/disabled editions
📊 Outputs edition-specific bug artifacts for Studio and QA scoring
🔄 Links fingerprint IDs to edition behavior for traceability

This enables accurate debugging across 1000s of micro-editions, reducing noise and focusing remediation where it truly matters.

🔧 Test Stabilization Workflow¶

This section explains how the Bug Investigator Agent contributes to test suite hardening by diagnosing unstable tests and suggesting precise stabilization strategies — such as retries, waits, rewrites, or test refactoring recommendations.

Stabilization is essential to eliminate flakiness, reduce false positives, and maintain confidence in autonomous QA outcomes.

🎯 Goal¶

Convert unstable or inconsistent test failures into stable, deterministic, and reliably passing tests — or isolate and disable them until corrected.

🧠 Stabilization Triggers¶

Trigger	Description
`flakyScore > threshold`	Test fails intermittently in past 3–5 builds
`diagnosedAsTestBug`	Root cause traced to test logic (e.g. missing waitFor)
`retrySuccess: true`	Test passed on second attempt with no code change
`error: element not found / too early`	Common signal of async race in UI test
`log suggests modal/render delay`	Observability signal indicates screen instability

📘 Example: `fix-recommendation.yaml` (Test Stabilization)¶

testId: SubmitFeedbackTest
fingerprintId: bug-f93b3e77
recommendation:
  action: patchTest
  fix:
    type: addWait
    selector: button[submit]
    condition: isVisible
    waitMs: 1000
  confidence: 0.93
reasoning: Element visible in span trace after 850ms; test failed at 500ms

🧩 Stabilization Options Suggested¶

Action	When Used
`addWait(selector)`	Element visible too late
`waitForState(condition)`	Async state not reached (e.g., loading=false)
`retryOnFailure(n)`	Test occasionally fails without logic difference
`debounceAssertions`	Chained async steps render too fast
`delayInput`	Typing/interaction faster than UI response
`refactorSelector`	DOM instability or race in mobile UI tree
`rewriteTest`	Logic fundamentally flawed or inconsistent
`quarantineTest`	Allow skip/ignore in CI until fix is applied

📄 Output Files Updated¶

File	Impact
`fix-recommendation.yaml`	Includes stabilization patch, rationale, confidence
`flaky-tests-index.yaml`	Marked with “patchSuggested: true”
`test-gap-report.yaml`	Lists unpatched flaky tests or unassigned bugs
`studio.qa.status.json`	Displays “stabilization pending” badge in test explorer

🔁 Stabilization Feedback Loop¶

flowchart TD
    FAIL[Test fails] --> QA[QA Agent]
    QA --> Bug[🐞 Bug Investigator]
    Bug -->|Diagnoses flake| Fix[Suggest stabilization]
    Fix --> TGen[Test Generator Agent]
    TGen -->|Applies patch| AutoTest[Patched Test]
    AutoTest --> QA

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🔧 Optional Retry Workflow (Policy-Driven)¶

Config	Result
`allowRetry: true`	Agent may issue retry before failing build
`autoPatchInMemory: true`	Agent can suggest in-place test patch (if confident)
`quarantinePolicy: aggressive`	Agent can skip test for N builds with warning badge

✅ Summary¶

The Bug Investigator Agent:

Detects test instability and suggests precise fixes
Outputs actionable stabilization patches (waits, retries, rewrites)
Tags flaky tests and reduces QA confidence accordingly
Integrates with Test Generator Agent for regeneration
Supports “quarantine until fixed” mode for pipeline reliability

This enables a self-healing QA ecosystem — where flaky tests don’t slow teams down, and automated stability evolves continuously.

🛠️ Code Annotation & Fix Suggestion¶

This section defines how the Bug Investigator Agent generates automated fix recommendations and code-level annotations when a regression, crash, or bug is traced to a specific logic issue in the source code.

This supports developer velocity, traceable debugging, and potential integration with code generation agents or GitHub Copilot workflows.

🎯 Fix Suggestion Goals¶

Identify likely buggy method, module, or file
Generate context-aware suggestions for fixes (code patch, null check, delay, etc.)
Add inline annotations in traceable form (code-annotations.yaml)
Feed recommendations to Studio, pull requests, or human triage agents

🧠 Input Signals for Fix Logic¶

Input	Use
Stack trace (top frames)	Determines root method or file
Git blame data	Links failure to last changed author/commit
Module metadata	Informs system boundary and domain area
Span logs	Indicates performance or state-based issues
Exception message	Identifies likely failure symptom
Retry success	Suggests code edge case or timing gap
FlakyTest + Crash → Same area	Elevates confidence of root cause

📘 Example: `fix-recommendation.yaml` (Code Fix Suggestion)¶

fingerprintId: bug-2f61db78
classification: Application Code Bug
suggestedFix:
  file: AppointmentService.cs
  method: ConfirmBooking
  line: 124
  suggestion: Add null check for `appointment.Patient`
  diffPreview: |
    if (appointment?.Patient == null) {
        throw new ArgumentException("Patient cannot be null");
    }
confidence: 0.95
reasoning: NullReferenceException traced to dereference of Patient object

📝 Optional: `code-annotations.yaml`¶

- file: AppointmentService.cs
  line: 124
  type: Error
  message: Possible null dereference (Patient object)
  linkedFingerprintId: bug-2f61db78
  suggestedFix: Add null check before usage

→ Used for Studio annotation tiles or inline PR comments.

📤 Consumers of Fix Suggestions¶

Consumer	Role
Code Reviewer Agent	May auto-inject annotation into code analysis reports
Studio Dashboard	Shows inline diff/fix preview under bug badge
Developer IDE	(Planned) SDK plugin to show suggestions inline
HumanOps Agent	For builds escalated with code root cause
Test Generator Agent	If fix is not viable, test rewrite suggested instead

🧩 Types of Fixes Supported¶

Fix Type	Trigger
`addNullCheck()`	NullReferenceException + parameter in trace
`delayExecution()`	Async rendering or span delay
`addErrorBoundary()`	Crash in frontend component tree
`refactorLogic()`	Wrong assertion logic in service layer
`patchTestInstead()`	When code is fine but test misfires
`suggestPRChange()`	Human-friendly patch shown for PR comment

🔄 Confidence Levels¶

Score	Behavior
`> 0.9`	Fix included in recommendation with justification
`0.75–0.9`	Fix included, flag added for human review
`< 0.75`	Fix withheld, escalate with `debug-handoff.md`

📊 Studio Fix View¶

🔎 Click bug → preview suggested fix
🧑‍💻 If fix maps to open PR, comment injected into file diff
✅ Option: "Apply Fix" (planned codegen integration)

✅ Summary¶

The Bug Investigator Agent:

Diagnoses failures down to code
Recommends precise fix strategies for known crash types
Emits structured fix-recommendation.yaml and code-annotations.yaml
Powers Studio insights, developer productivity, and agent collaboration

This closes the loop between QA diagnostics and real developer action, enabling agent-assisted debugging and code health improvement.

🔁 CI/CD Feedback Loop¶

This section outlines how the Bug Investigator Agent integrates into CI/CD pipelines to provide intelligent, traceable, and policy-respecting bug feedback. It enables:

Build pass/fail corrections
Retry logic for flaky tests
Regression memory enforcement
Pipeline noise suppression
Inline diagnostic summaries for dev workflows

🔁 Bug Investigation CI/CD Loop¶

flowchart TD
    CI[CI/CD Pipeline] --> QA[QA Engineer Agent]
    QA -->|Failure Trigger| Bug[🐞 Bug Investigator Agent]
    Bug -->|Fix/Retry Suggestion| CI
    Bug -->|Regression Confirmation| QA
    Bug -->|Annotation + Summary| PR

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🎯 Key CI/CD Feedback Capabilities¶

Function	Behavior
Retry Trigger	If test classified as flaky + policy allows, trigger auto-retry
False Positive Override	Known issue matched → downgrade to warning or allow pass
Regression Confirmation	Known fingerprinted bug confirmed → marks regression and halts release
Build Status Correction	Failed → retried and passed? Agent updates status to “pass with warning”
Diagnostic Summary Push	Posts `diagnostic-summary.md` as PR comment or pipeline artifact

📘 Example: CI Patch Snippet (GitHub Actions)¶

- name: Evaluate QA Result
  run: |
    if [ -f bug-fingerprint.json ]; then
      regression=$(jq -r .classification bug-fingerprint.json)
      confidence=$(jq -r .confidence bug-fingerprint.json)
      if [ "$regression" == "Flaky Test" ] && (( $(echo "$confidence > 0.9" | bc -l) )); then
        echo "ℹ️ Flaky test auto-retry permitted."
        exit 0
      fi
      if [ "$regression" == "Application Code Bug" ]; then
        echo "❌ Confirmed code bug. Failing build."
        exit 1
      fi
    fi

📂 Files Emitted to CI/CD Stage¶

File	Purpose
`bug-fingerprint.json`	Root cause & match info
`fix-recommendation.yaml`	Suggested patch or stabilization
`flaky-tests-index.yaml`	Retry eligible test IDs
`debug-handoff.md`	Summary to show in PR comment or dashboard
`studio.qa.bug.status.json`	Push to dashboard for test and build diagnostics

🧠 Retry Policy Integration¶

Policy Setting	Result
`qa.allowRetry = true`	Bug Agent can retry flaky tests before failing build
`bug.retryOnFlakyScore > 0.85`	Retry triggered automatically
`maxRetryAttempts = 2`	Retry capped to avoid loops

🧑‍💻 PR Feedback Example (Markdown)¶

### 🐞 Bug Diagnostic Summary
- **Test**: CancelAppointmentModalTest
- **Classification**: Flaky Test (UI Race)
- **Confidence**: 0.91
- **Suggested Fix**: Add waitFor on button rendering
- **Build Action**: Auto-retry passed ✅
- **Fingerprint**: `bug-2f61db78`

[See full diagnostic →](link-to-bug-fingerprint.json)

📊 Studio & DevOps View¶

Display	Info
Badge on test tile	Flaky / regression / unstable / resolved
Retry tracker	Shows when retry occurred and succeeded
Artifact log	See all outputs under `/qa-bugs/{buildId}/`
Test explorer	Filter by fingerprint, regression, fix suggested

✅ Summary¶

The Bug Investigator Agent:

🔁 Provides dynamic bug feedback to CI/CD pipelines
🧠 Applies retry, suppression, or escalation logic per bug type
📄 Posts summaries to PRs, DevOps dashboards, and Studio
✅ Ensures QA decisions remain actionable and context-aware across automation flows

This creates smarter pipelines, developer clarity, and traceable bug memory — without false alarms or flaky chaos.

🖥️ Studio Integration & Visual Debugging¶

This section describes how the Bug Investigator Agent integrates with the ConnectSoft Studio dashboard, making diagnostics human-visible, navigable, and actionable for developers, QA leads, and human reviewers.

Visual debugging allows teams to:

Spot trends and regressions faster
Review flaky or failing tests by module/screen
Understand suggested fixes directly in the Studio UI
Investigate edition-specific failures through Studio filters

🧩 Core Integration Points in Studio¶

View	Bug Data Displayed
Test Explorer	Flaky test badges, regression clusters, stability trends
QA Dashboard Tile	Build-wide bug summary with known issue links
Edition Matrix	Bugs isolated to specific editions/tenants
Debug Details Panel	Inline bug fingerprint, trace links, suggested fix
Trend Heatmap	Failure recurrence by test, module, or screen over time

📘 Example: `studio.qa.bug.status.json`¶

{
  "buildId": "bookingapp-v5.3.0",
  "platform": "flutter",
  "traceId": "trace-8912af1",
  "bugs": [
    {
      "testId": "CancelAppointmentModal",
      "fingerprintId": "bug-2f61db78",
      "classification": "Flaky Test",
      "matchConfidence": 0.91,
      "status": "auto-retried",
      "recommendedFix": "Add waitForVisible(button)",
      "occurrences": 4,
      "editionId": "vetclinic-blue"
    }
  ]
}

🧠 Studio UX Interactions Supported¶

Action	Result
🔎 Click test tile	Open `bug-fingerprint.json` with history and resolution tips
🧩 View test flakiness score	See time-series chart (instability trend)
🎯 Click “Apply Fix” (future)	Send suggested fix to codegen or test-gen agent
🟡 Hover regression badge	Show last seen build, recurrence %, edition flags
🧪 Filter tests	By bug classification, fingerprint ID, affected editions
💬 View summary	`diagnostic-summary.md` previewed inside modal window

🔄 Dashboard Update Triggers¶

Trigger	Dashboard Change
`bug-fingerprint.json` emitted	Adds regression cluster badge
`flaky-tests-index.yaml` updated	Adds “Flaky” icon to test view
`debug-handoff.md` created	Sends issue card to “Needs Human Review” panel
`fix-recommendation.yaml` valid	Shows fix preview with diff snippet

📎 Test Tile Badges¶

Badge	Meaning
🟡 “Flaky”	Detected flakiness with retryable logic
🔁 “Regression”	Repeating issue seen across builds
🧪 “Unstable”	Newly failing test with high variance
✅ “Patched”	Fix recommendation applied/test stabilized
🧭 “Edition Scope”	Only affects specific edition(s)
🛑 “Manual Review”	Escalated to HumanOps or QA team

-------------------------------------
🧠 Bug: CancelAppointmentModal

• Classification: Flaky Test
• Root: Modal not rendered within 500ms
• Suggestion: Add waitForVisible(button)
• Fingerprint: bug-2f61db78
• Edition: vetclinic-blue
• First Seen: v5.2.0
• Occurrences: 4 builds

[👁 View Logs]   [📎 Copy Fingerprint]   [🧰 Suggested Fix]
-------------------------------------

✅ Summary¶

The Bug Investigator Agent:

Integrates deeply with Studio’s QA and Test Explorer views
Visualizes bug fingerprints, regression clusters, and flaky behavior
Provides clear UI tiles and fix suggestions for human review
Enables edition-aware debugging through filtered dashboards

This empowers teams with a real-time, visual debugging console — powered entirely by AI-driven root cause analysis.

🧠 Memory & Learning from Past Bugs¶

This section explains how the Bug Investigator Agent builds and utilizes long-term memory to improve future bug diagnosis, reduce redundancy, and enable intelligent regression handling.

By learning from past bugs, the agent becomes faster, more accurate, and capable of cross-project diagnostic intelligence.

🎯 Objectives of Bug Memory¶

📚 Identify regressions seen before and suppress duplicates
🧩 Cluster test failures around shared root causes
🧪 Detect repeating flakiness patterns
🧠 Accelerate diagnosis with prior context and resolution strategies
📈 Improve confidence scoring across test + edition + trace dimensions

📦 Memory Components¶

Memory Store	Content
`bug-fingerprint-db`	Canonical representations of root causes (stack trace, module, error hash)
`regression-clusters.yaml`	Grouped history of regressions linked to fingerprint IDs
`flaky-tests-index.yaml`	Time-series based flakiness metadata per testId
`fix-recommendation-cache.json`	Previously generated fixes with outcomes
`known-bugs-index.vec`	Vector-based embedding index of historical errors for fuzzy matching
`edition-impact-map.yaml`	Bugs scoped to tenants/editions/platforms over time

📘 Sample: `bug-fingerprint-db` Entry¶

{
  "fingerprintId": "bug-2f61db78",
  "error": "Modal button not visible",
  "stackTrace": ["ModalDialog.tsx: line 122", "RenderScreen.tsx: line 87"],
  "testId": "CancelAppointmentModal",
  "platform": "flutter",
  "classification": "Flaky Test",
  "occurrences": 6,
  "lastSeen": "2025-05-15T14:03:22Z",
  "recommendedFix": "waitForVisible(button)"
}

🧠 How Memory Is Used¶

Use Case	Behavior
🔁 Regression re-detected	Linked to fingerprint → not re-diagnosed from scratch
🧪 Flaky test score update	Aggregates failure rates over last N builds
📤 Fix suggestion reuse	Pulls recent successful patches for same root
🔍 Search similar bugs	Uses vector embeddings to cluster stack trace similarity
🧭 Edition-based regression isolation	Memory-aware scoring avoids penalizing global QA on edition-specific bugs

🔄 Update Cycle¶

Trigger	Update
New fingerprint created	Stored in `bug-fingerprint-db`
Retry success with same root	Marked as flaky and suppressed
Fix accepted via Studio or PR	Mark fingerprint as “Resolved”
Escalated issue manually closed	Feedback loop updates memory state as `ClosedByHumanOps`

📊 Trend Insights Enabled by Memory¶

“This bug has occurred in 4 of the last 6 builds”
“FlakyScore: 0.91 — retried 3 times, passed twice”
“Regression first seen on bookingapp-v5.2.0, last seen now”
“This issue affected 3 editions: vetclinic-blue, wellness-lite, medscope-standard”

✅ Summary¶

The Bug Investigator Agent:

📚 Builds persistent memory of known bugs, flakiness, and root causes
🧠 Reuses prior learning to improve performance and reduce noise
🔁 Keeps all artifacts traceable by fingerprintId and editionId
🔧 Reduces repeated diagnostics and redundant CI/CD feedback

This makes ConnectSoft’s QA ecosystem cumulative, intelligent, and increasingly autonomous over time — using real software factory learning loops.

🤝 HumanOps & Dev Collaboration Hooks¶

This section defines how the Bug Investigator Agent escalates unresolved bugs to human stakeholders, supports manual triage, and provides structured collaboration hooks for developers, QA leads, and HumanOps agents.

When automation hits its limit — ambiguous trace, no fingerprint match, or low-confidence diagnosis — the agent emits clear, structured artifacts for efficient human resolution.

🧭 When Human Collaboration Is Triggered¶

Scenario	Trigger
❓ Ambiguous root cause	Confidence score < 0.75
🧩 Unknown stack trace	No match in known bug vector index
🔁 Repeated unstable failures without clear pattern	Manual classification needed
🚫 Platform/Edition-specific issue outside test scope	Requires business/UX triage
👷 Suggested fix needs developer decision	Refactor or logic rewrite proposed
🛑 Manual override required (e.g., QA policy mandates it)	HumanOps must approve or suppress

📘 Output: `debug-handoff.md`¶

# 🐞 Debug Handoff – Requires Human Review

**Test:** ConfirmBookingAnalyticsTest  
**Classification:** Unknown (unmapped error signature)  
**Confidence:** 0.62  
**Trace ID:** trace-a193bd71  
**Edition:** wellness-lite  
**Fingerprint:** Not found

### Stack Summary
``BookingWorkflow.cs: Line 92 → Null when accessing Session.User``

### Logs
- `No token found in context`
- `Unhandled Exception: ArgumentNullException`

### Recommended Action
Review affected module and test to validate whether user session is expected. No automatic fix available.

📎 Handoff Includes¶

File	Description
`debug-handoff.md`	Summary of error, trace, recommendation, and unknowns
`bug-fingerprint.json`	Empty or partial — indicates new issue
`studio.qa.bug.status.json`	Flags bug as `status: needs-human-review`
`manual-review-needed.md` (optional)	Triggers HumanOps escalation in Studio/PR

📤 Collaboration Surfaces¶

Channel	Action
Studio	Debug tile appears in QA dashboard with “🚧 Manual Review Required”
PR	Comment posted linking to `debug-handoff.md`
DevOps	Build marked “requires human validation” before promotion
HumanOps Agent	Subscribes to escalated issues for triage queue
Slack/Email/Webhook (optional)	Notification emitted for critical unresolved bugs

👤 HumanOps Actions Supported¶

Action	Effect
✅ Approve override	Marks issue as “Allowed” or “Low Risk” for this build
🧪 Request re-analysis	Agent re-runs fingerprinting with updated inputs
📝 Annotate test/module	Feedback stored in `studio.qa.annotations.json`
🚧 Quarantine test	Marks test as “Skip until fixed” in `flaky-tests-index.yaml`
🔧 Submit fix manually	Updates `bug-fingerprint-db` with resolved signature and patch applied

📊 Studio Dashboard UX¶

🟡 Yellow badge on affected test tile
“Manual Review” panel for unresolved bugs
Click to expand stack trace, traceId, recommended action
Approve, quarantine, or escalate options available via UI buttons

✅ Summary¶

The Bug Investigator Agent:

🤝 Escalates ambiguous or unresolved bugs to humans in a structured, traceable way
📄 Emits debug summaries, fingerprint metadata, and rationale for review
🧑‍💻 Enables QA engineers and developers to close the loop on issues automation cannot resolve
🔁 Learns from human annotations to improve future triage

This ensures a hybrid human–AI QA loop, balancing speed with precision — and empowering developers through transparency and insight.

🧭 Final Blueprint & Future Expansion¶

This final cycle consolidates the Bug Investigator Agent’s architecture, flow, and agentic interfaces, and outlines future extensions that will elevate it from a powerful triage tool into a fully autonomous software debugging assistant within ConnectSoft’s AI Software Factory.

🧱 Final Blueprint Diagram¶

flowchart TD
    QA[QA Engineer Agent] -->|Failures, Regressions| Bug[🐞 Bug Investigator Agent]
    Bug --> CI[CI/CD Agent]
    Bug --> Studio[Studio Agent]
    Bug --> TestGen[Test Generator Agent]
    Bug --> Human[HumanOps Agent]
    Bug --> Code[Code Reviewer Agent]

    subgraph Outputs
      BF[bug-fingerprint.json]
      FR[fix-recommendation.yaml]
      FI[flaky-tests-index.yaml]
      DR[debug-handoff.md]
      SA[studio.qa.bug.status.json]
    end

    Bug --> Outputs

Hold "Alt" / "Option" to enable pan & zoom

🧠 Core Capabilities Recap¶

Capability	Description
🧪 Test failure triage	Classify, explain, and track every failed test
🔁 Regression tracking	Memory-based detection of repeated root causes
💥 Crash diagnostics	Parse logs, spans, stack traces into actionable issues
🔧 Fix recommendation	Suggest retries, test patches, or code-level diffs
🔁 CI/CD integration	Retry logic, pass/fail overrides, suppress flaky failures
📊 Studio integration	Visual QA dashboard with bug traceability
🧠 Memory + vector similarity	Learn from historical bug patterns and fingerprint clusters
🤝 Human review hooks	Emit summaries and artifacts for unresolved issues

📦 Artifact Summary¶

Artifact	Purpose
`bug-fingerprint.json`	Canonical ID for a root cause
`fix-recommendation.yaml`	Concrete action to stabilize or repair
`flaky-tests-index.yaml`	Longitudinal memory of test instability
`regression-cluster.yaml`	Group of bugs with same fingerprint
`debug-handoff.md`	Human-readable escalation artifact
`studio.qa.bug.status.json`	Dashboard-friendly diagnostic metadata

🔮 Future Expansion Opportunities¶

✅ Near-Term Enhancements¶

Feature	Benefit
LLM-assisted root cause explanations	More human-readable diagnostics
Test replay & slow motion trace diff	Deep debugging of async UI behavior
Heuristic flakiness suppression logic	More nuanced retry scoring
Bug auto-resolution tagging	Based on commit diff linked to fingerprintId

🌐 Mid-Term Agentic Extensions¶

New Agent	Role
Triage Assistant Agent	Assist developers in real time during fix/PR
Fix Generation Agent	Use AI to synthesize full patch for simple regressions
Bug Cluster Explorer Agent	Navigate bugs by symptom, module, edition, or API contract drift
Live RCA with Simulation	Auto-run test with logging enabled to reproduce issue

🚀 Long-Term Vision¶

A fully autonomous debugging agent capable of:

Diagnosing new bugs

Suggesting patches or PRs

Quarantining unstable code paths

Recommending observability instrumentation

Learning continuously across tenants, features, and architectures

✅ Summary¶

The Bug Investigator Agent:

🧠 Diagnoses, explains, and tracks every failure and regression
🔁 Builds persistent memory for recurring bugs and flaky tests
🔧 Suggests fixes or stabilization paths
📤 Feeds CI/CD, Studio, QA, TestGen, and HumanOps workflows
🧭 Evolves as a self-improving diagnostic assistant

It is the diagnostic core of ConnectSoft’s QA intelligence layer, enabling trustworthy, automated quality enforcement at massive scale.

🐞 Bug Investigator Agent Specification¶

🧠 Purpose & Position in QA Ecosystem¶

🧭 Strategic Role in the QA Ecosystem¶

🔁 Bug Lifecycle Flow Position¶

🧱 What the Bug Investigator Agent Guarantees¶

👥 Agents It Collaborates With¶

🎯 Strategic Value to ConnectSoft¶

✅ Summary¶

🧭 Core Responsibilities¶

📂 Bug Investigation Artifact Catalog¶

📘 Example: fix-recommendation.yaml¶

🧩 Decision-Making Modes¶

🤖 Output Consumers¶

✅ Summary¶

📥 Inputs Consumed¶

📂 Primary Input Artifacts¶

🧠 Inferred Inputs (via Kernel Memory or Event Graph)¶

📘 Sample: test-results.json (subset)¶

📘 Sample: unhandled-exceptions.json¶

🧩 Optional Runtime Hints (Advanced Inputs)¶

🔄 Input Types Summary¶

✅ Summary¶

📤 Outputs Produced¶

📦 Core Output Artifacts¶

📘 Example: bug-fingerprint.json¶

📘 Example: fix-recommendation.yaml¶

📘 Example: diagnostic-summary.md¶

🎯 Output Consumers¶

📎 Trace Tags in Outputs¶

✅ Summary¶

🔄 Execution Flow¶

🧭 High-Level Process Flow¶

🪜 Execution Phase Details¶

🧠 Execution Behavior by Trigger¶

📘 Sample Internal State Snapshot¶

🧑‍💻 Escalation Path¶

🧾 Key Features of the Flow¶

✅ Summary¶

🧩 Semantic Kernel Skills¶

🧠 Core Semantic Kernel Skills¶

📘 Skill Composition Example¶

📘 Sample Skill Output – SuggestFixActionSkill¶

🧩 Reusable Skill Integration¶

🔄 Skill Execution with Context¶

✅ Summary¶

⚙️ Failure Type Classification¶

🧩 Primary Failure Categories¶

📘 Classification Output Example¶

🧠 Classification Criteria (by Skill)¶

🧑‍💻 Developer View (Studio or PR Summary)¶

🔄 Classification Impact on Pipeline¶

📎 Classification Tags in Artifacts¶

✅ Summary¶

💥 Crash Analysis & Log Inference¶

🧩 Crash & Log Inputs¶

🧠 Crash Parsing & Pattern Matching¶

📘 Example: Parsed Exception (unhandled-exceptions.json)¶

📘 Example: Inferred Crash Bug Output¶

🔍 Crash Location Attribution¶

🔬 Log Analysis Techniques¶

🧑‍💻 Developer-Friendly Debug Summary¶

🔄 Action Routing from Crash¶

✅ Summary¶

🔁 Flaky Test Detection & Tagging¶

🧪 What Is a Flaky Test?¶

🧠 Detection Signals¶

📘 Example: Flaky Test Score Output¶

📘 flaky-tests-index.yaml¶

🧩 Flakiness Score Formula (Heuristic)¶

🔁 Retry Handling¶

🧱 Outputs Affected by Flakiness Detection¶

🧠 Agent Memory¶

✅ Summary¶

🔁 Regression Fingerprinting & Tracking¶

🧠 What Is a Regression Fingerprint?¶

🧩 Fingerprint Sources¶

📘 Example: bug-fingerprint.json¶

📘 Example: regression-cluster.yaml¶

🔁 Fingerprinting Process¶

🧠 Bug Memory Storage¶

📘 Example: `fix-recommendation.yaml`¶

📘 Sample: `test-results.json` (subset)¶

📘 Sample: `unhandled-exceptions.json`¶

📘 Example: `bug-fingerprint.json`¶

📘 Example: `fix-recommendation.yaml`¶

📘 Example: `diagnostic-summary.md`¶

📘 Sample Skill Output – `SuggestFixActionSkill`¶

📘 Example: Parsed Exception (`unhandled-exceptions.json`)¶

📘 `flaky-tests-index.yaml`¶

📘 Example: `bug-fingerprint.json`¶

📘 Example: `regression-cluster.yaml`¶

🧩 Example: `edition-config.yaml`¶

📘 Sample: `edition-test-violation.yaml`¶

📘 Example: `fix-recommendation.yaml` (Test Stabilization)¶

📘 Example: `fix-recommendation.yaml` (Code Fix Suggestion)¶

📝 Optional: `code-annotations.yaml`¶

📘 Example: `studio.qa.bug.status.json`¶

📘 Sample: `bug-fingerprint-db` Entry¶

📘 Output: `debug-handoff.md`¶