Software Development
Metric Validation Harness
Empirically validates a software metric before trusting or optimizing it — point it at any candidate metric (a command that takes a path and prints one number) plus a corpus, and it runs experiments that try to falsify each property a good metric must have. Checks determinism (same input, same number across runs and hash seeds), invariance to cosmetic edits (also an anti-gaming probe), monotonicity under construct-increasing edits, discrimination, robustness on edge inputs, near-linear tractability, and construct validity (convergent, discriminant vs LOC, predictive AUC, lift over a baseline). Trigger whenever someone proposes, reviews, tunes, or ships a metric, score, or index, asks "is this metric any good", suspects a score tracks LOC or jumps between runs, or builds a deterministic optimization target. It is the empirical companion to the deterministic-metric-design skill and is read-only.
- Publisher / source
- pproenca/dot-skills (found on skills.sh)
- Upstream record
- pproenca/dot-skills
- Version
- Version 2
- Published
- Publication review
- Approved for publication
- Content fingerprint
abdbfbf1ada1b0ada4b5f1fbdcb06ecb524eb3b7d7b675b15a460e642f7684cd