2026 WORKFLOW: FROM WRITING CODE TO FORENSIC ENGINEERING
A recent video argues engineers will spend less time hand-writing code and more time specifying behavior, generating tests, and verifying AI-produced changes—"f...
A recent video argues engineers will spend less time hand-writing code and more time specifying behavior, generating tests, and verifying AI-produced changes—"forensic engineering." For backend/data teams, this means using AI to read large codebases and pipelines, propose patches, and auto-generate characterization tests, while humans review traces, diffs, and test outcomes.
Shifts effort from implementation to verification, potentially speeding delivery on complex or legacy codebases.
Emphasizes tests and traceability to reduce regression risk from AI-generated changes.
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terminal
Pilot AI-driven characterization test generation on a critical service or pipeline and measure flakiness and coverage deltas.
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Run an LLM-assisted PR workflow (AI proposes patch + tests), gate on CI, and track review time and defect escape rate.
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