Lightweight Developer-Friendly Explanations for Software Defect Prediction

Abstract

Software defect prediction using machine learning models is widely adopted to identify defects before release and to guide testing efforts. However, the black-box nature of these models limits their trustworthiness and usability among developers. To address this issue, recent research has focused on providing explanations for model predictions, helping developers understand why a prediction was made and increasing their confidence in the results. Despite this progress, current explanation techniques for software defects are often computationally intensive and produce complex, overwhelming outputs that are difficult for developers to interpret and act upon. To overcome these challenges, this paper proposes a lightweight, developer-friendly, rule-based explanation strategy that combines the strengths of two popular local explanation methods: LIME and Anchor. This is done by extracting the intersection of conditions common between LIME and Anchor. The proposed approach generates concise, high-confidence explanations that preserve fidelity while enhancing interpretability. We evaluated our approach on 1,000 defective instances from the NASA PROMISE Ant dataset. Compared to standalone LIME and Anchor outputs, the reduced explanations provided more actionable insights with fewer and more consistent conditions. Overall, the proposed method offers a practical and scalable path toward more interpretable and developer-friendly defect prediction.