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Process metrics, valued for their language independence and ease ofcollection, have been shown to outperform product metrics in defect prediction.Among these, change entropy (Hassan, 2009) is widely used at the file level andhas proven highly effective. Additionally, past research suggests thatco-change patterns provide valuable insights into software quality. Building onthese findings, we introduce Co-Change Graph Entropy, a novel metric thatmodels co-changes as a graph to quantify co-change scattering. Experiments oneight Apache projects reveal a significant correlation between co-changeentropy and defect counts at the file level, with a Pearson correlationcoefficient of up to 0.54. In filelevel defect classification, replacing changeentropy with co-change entropy improves AUROC in 72.5% of cases and MCC in62.5% across 40 experimental settings (five machine learning classifiers andeight projects), though these improvements are not statistically significant.However, when co-change entropy is combined with change entropy, AUROC improvesin 82.5% of cases and MCC in 65%, with statistically significant gainsconfirmed via the Friedman test followed by the post-hoc Nemenyi test. Theseresults indicate that co-change entropy complements change entropy,significantly enhancing defect classification performance and underscoring itspractical importance in defect prediction.

Co-Change Graph Entropy: A New Process Metric for Defect Prediction