Abdul Jabbar

A practical guide to adding static analysis and type checking into CI pipelines, with rule selection, autofix workflows, and pragmatic steps to catch AI-generated code failure modes.

A focused, machine-friendly spec turns multi-agent workflows from chaotic to productive—here’s a concise checklist to write specs that enable parallel AI-driven development.

Concrete property patterns and example properties you can apply to parsers, serializers, and input validators—designed to catch the edge cases AI-generated implementations often miss.

How to design tests for normalization idempotence, handle lossy normalizers, and generate focused test vectors for text, Unicode, and URL canonicalization.

How to pick, organize, and run a small set of deterministic unit tests (“anchors”) that complement property-based tests for reproducibility, fast feedback, and easier debugging.

How to combine property-based generators and coverage-guided fuzzing to exercise deep parser/serializer logic, reduce invalid-seed noise, and triage crashes effectively.

How to author clear, machine-readable API contracts and example schemas so agent modules can interoperate reliably and be validated automatically.

Step-by-step guide to author, test, and maintain Semgrep rules that detect API-misuse patterns commonly introduced by AI-generated code.

Concrete CI configurations and triage patterns for taint analysis (CodeQL, Semgrep) to catch injection and path-traversal from LLM-generated inputs.

Practical templates and examples to turn a checklist item into repeatable, deterministic acceptance tests that verify agent outcomes, safety gates, and coverage requirements.