Abstract
Systems and methods are described for automated assessment of code review quality using review comments, reviewer actions, reactions, and diff context. A pipeline identifies human reviewers, extracts per-reviewer comment threads, and executes parallel sub-agent analyses to produce structured signals including impact and relevance scores and comment attributes. A deterministic ruleset classifies each comment into HIGH, MEDIUM, LOW, or OUT_OF_SCOPE. A reviewer score is computed using weighted counts of classified comments with a logarithmic normalization term to provide diminishing returns, and may include additive bonuses such as code suggestions, reactions, and request-changes actions. Multi-layer integrity safeguards may include concentration ratio thresholds, Z-score outlier detection, and rubber-stamp detection. Results are stored for aggregation and used to generate dashboards, leaderboards, badges, quadrant classifications, and trajectory analytics
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Anonymous, "Systems and Methods for Automated Code Review Quality Assessment Using Hybrid Classification and Logarithmic Scoring with Anti-Gaming Safeguards", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10756