Leveraging Commit Size Context and Hyper Co-Change Graph Centralities for Defect Prediction

TL;DR

This paper introduces commit size aware process metrics and hypergraph centralities to improve file-level defect prediction, demonstrating enhanced performance across multiple projects and classifiers.

Contribution

It proposes a novel approach combining commit size profiles and hyper co-change graph centralities, capturing higher-order change semantics for defect prediction.

Findings

Replacing scalar process metrics with commit size aware vectors improves prediction accuracy.

Hypergraph centralities effectively quantify size-aware node importance.

The approach outperforms traditional models across nine Apache projects.

Abstract

File-level defect prediction models traditionally rely on product and process metrics. While process metrics effectively complement product metrics, they often overlook commit size the number of files changed per commit despite its strong association with software quality. Network centrality measures on dependency graphs have also proven to be valuable product level indicators. Motivated by this, we first redefine process metrics as commit size aware process metric vectors, transforming conventional scalar measures into 100 dimensional profiles that capture the distribution of changes across commit size strata. We then model change history as a hyper co change graph, where hyperedges naturally encode commit-size semantics. Vector centralities computed on these hypergraphs quantify size-aware node importance for source files. Experiments on nine long-lived Apache projects using five popular classifiers show that replacing scalar process metrics with the proposed commit size aware vectors, alongside product metrics, consistently improves predictive performance. These findings establish that commit size aware process metrics and hypergraph based vector centralities capture higher-order change semantics, leading to more discriminative, better calibrated, and statistically superior defect prediction models.