An Empirical Study on the Stability of Explainable Software Defect Prediction

Explaining the results of software defect prediction (SDP) models is practical but challenging. Jiarpakdee et al. proposed using two model-agnostic techniques (i.e., LIME and BreakDown) to explain prediction results. They showed that model-agnostic techniques can achieve remarkable performance and that the generated explanations can assist developers in understanding the prediction results. However, the fact that they examined these model-agnostic techniques only under a specific SDP setting calls into question their reliability on SDP models under various settings. In this paper, we set out to investigate the reliability and stability of model-agnostic-based explanation generation approaches on SDP models under different settings, e.g., different data sampling techniques, machine learning classifiers, and prediction scenarios used when building SDP models. We use model-agnostic techniques to generate explanations for the same instance under various SDP models with different settings and then check the stability of the generated explanations for the instance. We reused the same defect data and experiment configurations from Jiarpakdee et al. in our experiments. The results show that the examined model-agnostic techniques generate inconsistent explanations under different SDP settings for the same test instances. Our user case study further confirms that inconsistent explanations can significantly affect developers' understanding of the prediction results, which implies that the model-agnostic techniques can be unreliable for practical explanation generation under different SDP settings. To conclude, we urge a revisit of existing model-agnostic-based studies in software engineering and call for more research in explainable SDP toward achieving stable explanation generation.