Despite advances in high-performance Software Defect Prediction (SDP) models, practitioners remain hesitant to adopt them due to opaque decision-making and a lack of actionable insights. Recent research has applied various explainable AI (XAI) techniques to provide explainable and actionable guidance for SDP results to address these limitations, but the trustworthiness of such guidance for practitioners has not been sufficiently investigated. Practitioners may question the feasibility of implementing the proposed changes, and if these changes fail to resolve predicted defects or prove inaccurate, their trust in the guidance may diminish. In this study, we empirically evaluate the effectiveness of current XAI approaches for SDP across 32 releases of 9 large-scale projects, focusing on whether the guidance meets practitioners' expectations. Our findings reveal that their actionable guidance (i) does not guarantee that predicted defects are resolved; (ii) fails to pinpoint modifications required to resolve predicted defects; and (iii) deviates from the typical code changes practitioners make in their projects. These limitations indicate that the guidance is not yet reliable enough for developers to justify investing their limited debugging resources. We suggest that future XAI research for SDP incorporate feedback loops that offer clear rewards for practitioners' efforts, and propose a potential alternative approach utilizing counterfactual explanations.

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.