Model Contribution Rate Theory: An Empirical Examination

TL;DR

This paper introduces the mhMCR method, a statistical framework that refines contribution metrics to accurately measure developer productivity by filtering out anomalies and automated changes, validated across multiple repositories.

Contribution

It presents the novel mhMCR methodology that improves productivity measurement by focusing on continuous, meaningful contributions and excluding distortions from automated or delayed commits.

Findings

Enhanced precision in identifying sustained developer activity

Improved productivity measurement accuracy across repositories

Actionable insights for team performance optimization

Abstract

The paper presents a systematic methodology for analyzing software developer productivity by refining contribution rate metrics to distinguish meaningful development efforts from anomalies. Using the Mean-High Model Contribution Rate (mhMCR) method, the research introduces a statistical framework that focuses on continuous contributions, mitigating distortions caused by tool-assisted refactoring, delayed commits, or automated changes. The methodology integrates clustering techniques, commit time deltas, and contribution sizes to isolate natural, logical work patterns and supports the accurate imputation of effort for contributions outside these patterns. Through empirical validation across multiple commercial repositories, the mhMCR method demonstrates enhanced precision in productivity measurement in identifying sustained developer activity. The findings provide actionable insights for optimizing team performance and workflow management in modern software engineering practices.