Validation intelligence for CI — attested test-selection with verifiable confidence bounds, failure-signature RCA, and analytics. Contract-compatible with agentsensory.
Project description
assaylab
Validation intelligence for CI. Attested test-selection with verifiable confidence bounds, failure-signature root-cause analysis, and analytics that support data-driven engineering decisions.
Status: name reserved, implementation in progress.
What it does (planned)
Validation intelligence & analytics
- Failure-signature clustering over historical CI/test results.
- Automated root-cause analysis and flaky-vs-real classification.
- Predictive failure forecasting and risk identification.
- A dashboard for data-driven engineering decisions.
Test optimization
- Risk-based test selection and prioritization from code-change diffs.
- Coverage-gap and redundancy detection.
- Runtime reduction that preserves a stated confidence bound.
Automation
- LLM-assisted test generation from requirements and code changes.
- Adaptive / self-healing execution, gated behind a verdict layer.
The distinctive idea
Most test-optimization tools give you a speedup and ask you to trust it.
assaylab emits a signed receipt that bounds the confidence lost when it
reduces a suite: ran subset S → probability of missing regression-class C ≤ ε,
with an attested, checkable proof. Speedup with a formal confidence claim.
Verdicts follow the agentsensory contract (Report = verdict + grounded issues
- Handoff), so results are portable and auditable.
MIT © 2026 Amit Patole
— amitpatole
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