Breaking New Ground in Software Defect Prediction: Introducing Practical and Actionable Metrics with Superior Predictive Power for Enhanced Decision-Making

TL;DR

This paper introduces human error-based metrics for software defect prediction at the method level, demonstrating superior predictive power and practical benefits over traditional code and history metrics through analysis of large open-source projects.

Contribution

It proposes a novel human error-inspired framework and metrics for defect prediction, outperforming existing code and commit history metrics in accuracy and explainability.

Findings

Proposed metrics outperform state-of-the-art in prediction accuracy.

Novel metrics provide better importance and explainability.

Framework enables actionable insights for practitioners.

Abstract

Software defect prediction using code metrics has been extensively researched over the past five decades. However, prediction harnessing non-software metrics is under-researched. Considering that the root cause of software defects is often attributed to human error, human factors theory might offer key forecasting metrics for actionable insights. This paper explores automated software defect prediction at the method level based on the developers' coding habits. First, we propose a framework for deciding the metrics to conduct predictions. Next, we compare the performance of our metrics to that of the code and commit history metrics shown by research to achieve the highest performance to date. Finally, we analyze the prediction importance of each metric. As a result of our analyses of twenty-one critical infrastructure large-scale open-source software projects, we have presented: (1) a human error-based framework with metrics useful for defect prediction at method level; (2) models using our proposed metrics achieve better average prediction performance than the state-of-the-art code metrics and history measures; (3) the prediction importance of all metrics distributes differently with each of the novel metrics having better average importance than code and history metrics; (4) the novel metrics dramatically enhance the explainability, practicality, and actionability of software defect prediction models, significantly advancing the field. We present a systematic approach to forecasting defect-prone software methods via a human error framework. This work empowers practitioners to act on predictions, empirically demonstrating how developer coding habits contribute to defects in software systems.