Estimating Uncertainty in Line-Level Defect Prediction via Perceptual Borderline Oversampling

Abstract

Software defect prediction aims to identify potentially defective software modules using various techniques, while fine-grained line-level defect prediction can pinpoint defective lines of code. This helps developers promptly discover and fix errors, thereby enhancing the efficiency of testing and code review. However, previous studies often overlook the impact of characterizing noise and the skewed distribution of defect knowledge in software projects, making it difficult for current methods to achieve satisfactory accuracy and cost-effectiveness in software defect prediction. To address these challenges, we propose a model named EU-LLDP, which effectively resolves the issue of low cost-effectiveness in line-level defect prediction models. Specifically, the EU-LLDP model consists of two main components: the defect mining component mines the most valuable defect knowledge from numerous software defects using prediction probability matrices, noise labels, and the borderline information of code vectorizations. The adaptive resampling component samples valuable defect knowledge through the density distribution of defect knowledge, thereby making full use of existing defect knowledge and improving the cost-effectiveness of line-level software defect prediction models. Seven comprehensive experiments were conducted on 32 defect datasets from 9 Java open source systems using file-level prediction models and line-level defect prediction models to evaluate the effectiveness of the EU-LLDP model. The EU-LLDP model improves the state-of-the-art file-level defect prediction model in terms of Balanced Accuracy by 9.87%, the MCC by 38.09%, and enhances the state-of-the-art line-level defect prediction method in terms of Recall@Top20%LOC by 44.16%, and Effort@Top20%Recall by 17.62%. These results fully demonstrate the effectiveness of EU-LLDP in improving the accuracy and cost-effectiveness of Software defect prediction.