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Know which areas of the codebase are likely to break before they do.

AI defect prediction surfaces high-risk modules from change patterns, complexity, and historical defect data.

Defects don't distribute uniformly. Most defects cluster in a small fraction of modules. Stride's defect-prediction model scores every module by risk and tells reviewers which PRs deserve careful eyes. It's the same model that's used to surface 'review carefully' at PR time and to plan regression-test investments.

What makes most defect prediction inaccurate?

Engineering managers know which areas of the codebase break. Usually 10-15% of the modules account for 60-70% of defects. But the knowledge lives in 2-3 senior engineers' heads. When those engineers are on PTO or move teams, the institutional memory walks. New PRs into the high-risk modules don't get the extra review they need, and predictable regressions ship.

How does Stride flag the modules most likely to regress before they ship?

Stride trains a defect-prediction model from your existing defect history + code metrics (cyclomatic complexity, churn, age, ownership concentration). Every module gets a risk score that updates as the codebase evolves. At PR review time, modules in the top risk quintile flag for extra attention. At sprint planning time, stories touching high-risk modules get a risk indicator that informs sizing.

  • Per-module risk score from history + complexity + churn + ownership
  • PR-time alerts when touching high-risk modules
  • Sprint-planning indicators for risk-weighted story sizing
  • Risk register: top 10 highest-risk modules with the patterns driving the score
  • Intervention suggestions: which modules need refactoring, more tests, or owner rotation
  • Time-trend: are we accumulating or paying down code risk
Best for

Mid-large engineering organisations (50+ engineers) with enough defect history to train a meaningful model and enough scale that institutional memory is a problem.

Not for

Small teams or new codebases. The model needs 6-12 months of defect history to be predictive; without it, the score collapses to "high complexity = risky", which you already know.

Frequently asked

How much defect history does the model need?
Useful predictions start at ~50 closed defects. Strong predictions at ~200+. Newer codebases with little defect history fall back to complexity + churn signals (which are coarser but still useful).
Is this a black box?
No. Every module risk score comes with the contributing factors visible (recent churn 35%, complexity score 84, owner concentration high, etc.). You can audit why a module scored as it did.
What about modules where the model is wrong?
Override per module. Mark as 'low risk despite the score' or 'high risk despite the score' with a rationale. The override carries forward and the model learns from the correction over time.
How does this compare to GitHub Copilot review suggestions?
Different layer. Copilot reviews a specific diff's code; defect prediction operates at the module level over time. Both are complementary. Copilot catches local code issues, Stride flags 'this PR touches an area that historically breaks'. Use both.