Over the past fifty years, numerous software defect prediction (SDP) approaches have been proposed. However, the ability to explain why predictors make certain predictions remains limited. Explainable SDP has emerged as a promising solution by using explainable artificial intelligence (XAI) methods to clarify the decision-making processes of predictors. Despite this progress, there is still significant potential to enhance the reliability of existing approaches. To address this limitation, we treat defect prediction and the corresponding interpretation as two distinct but closely related tasks and propose a joint learning framework that allows for the simultaneous training of the predictor and its interpreter. The novelty of our approach lies in two main aspects: 1. We design feedback loops that convey the decision-making logic from the predictor to the interpreter. This ensures a high level of conciseness in decision logic and feature engineering for both the predictor and the interpreter, enabling the interpreter to achieve reliable local and global interpretability. 2. We incorporate the interpretation results as a penalty term in the loss function of the joint-learning framework. This not only improves the accuracy of the predictor but also imposes a stronger constraint on the reliability of the interpreter. We validated our proposed method against several existing explainable SDPs across multiple datasets. The results demonstrate its effectiveness in both interpretation and defect prediction. The source code for the proposed method is available at: https://github.com/BugPredictor/software-defect-prediction.git

翻译：过去五十年间，软件缺陷预测（SDP）领域已涌现出大量研究方法。然而，现有方法在解释预测器为何做出特定预测方面的能力仍然有限。可解释性SDP通过运用可解释人工智能（XAI）技术阐明预测器的决策过程，已成为一种具有前景的解决方案。尽管已取得进展，现有方法在可靠性方面仍有较大提升空间。为突破此局限，我们将缺陷预测及其解释视为两个独立但紧密关联的任务，提出一种允许预测器与其解释器同步训练的联合学习框架。本研究的创新性主要体现在两个方面：1. 我们设计了从预测器向解释器传递决策逻辑的反馈回路，确保预测器与解释器在决策逻辑和特征工程方面均保持高度简洁性，从而使解释器能够实现可靠的局部与全局可解释性。2. 我们将解释结果作为惩罚项纳入联合学习框架的损失函数，这不仅提升了预测器的准确性，也对解释器的可靠性施加了更强约束。我们在多个数据集上通过对比现有可解释SDP方法验证了所提方法的有效性，实验结果表明该方法在解释能力和缺陷预测性能方面均表现优异。本方法的源代码已公开于：https://github.com/BugPredictor/software-defect-prediction.git