Code-Structure-Aware Regression Finder Using Dependency Graphs

C6/10April 1, 2026

WhatA CI/CD tool that combines code dependency graph analysis with Bayesian inference to automatically identify the most likely culprit commits for regressions, reducing debugging time by prioritizing structurally risky changes.

SignalMultiple commenters independently arrived at the idea that weighting commits by their structural impact (how many transitive dependents they touch) dramatically improves bug-finding accuracy, suggesting strong latent demand for tools that understand code topology beyond simple line-diff analysis.

Why NowCodebases have grown so large and interconnected that flat commit histories are insufficient for reasoning about impact; static analysis and LSP tooling have matured enough to make real-time dependency graph construction feasible at scale.

MarketEnterprise engineering orgs with 50+ developers paying $50-200K/yr for CI optimization tools; TAM within the broader $5B CI/CD market. Competes with Sourcegraph (code search) and Launchable (test intelligence) but neither combines structural analysis with probabilistic bisection.

MoatThe entity dependency graph becomes more valuable as it learns from historical regressions in a codebase — which structural patterns actually correlate with bugs — creating a flywheel of improving predictions unique to each customer's repo.

Show HN: Git bayesect – Bayesian Git bisection for non-deterministic bugs View discussion ↗ · Article ↗ · 302 pts · April 1, 2026

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