NeuroJIT: Improving Just-In-Time Defect Prediction Using Neurophysiological and Empirical Perceptions of Modern Developers

Abstract

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Modern developers make new changes based on their understanding of the existing code context and review these changes by analyzing the modified code and its context (i.e., commits). If commits are difficult to comprehend, the likelihood of human errors increases, making it harder for practitioners to identify commits that might introduce unintended defects. Nevertheless, research on predicting defect-inducing commits based on the difficulty of understanding them has been limited. In this study, we present a novel approach NeuroJIT, that leverages the correlation between modern developers' neurophysiological and empirical reactions to different code segments and their code characteristics to find the features that can capture the understandability of each commit. We investigate the understandability features of NeuroJIT in three key aspects: (i) their correlation with defect-inducing risks; (ii) their differences from widely adopted features used to predict these risks; and (iii) whether they can improve the performance of just-in-time defect prediction models. Based on our findings, we conclude that neurophysiological and empirical understandability of commits can be a competitive predictor and provide more actionable guidance from a unique perspective on defect-inducing commits.