A ranking-based approach for effort-aware software defect prediction.

Abstract

Predicting software defects is a critical aspect of software quality assurance, as early identification of potential faults enables better resource allocation and ensures high-quality software production. Effort-aware defect prediction enhances testing and maintenance by prioritizing software modules that maximize defect detection while minimizing inspection effort. Traditional methods typically predict defect probability or defect density and rank modules accordingly, often optimizing metrics such as the Proportion of Found Bugs at 20% LOC (PofB@20%) using linear regression models. In this study, we proposed a novel ranking approach that directly predicts the total number of defects per software module using a Random Forest Regressor. Each module is assigned a custom score that balances two objectives: selecting modules with more predicted defects and minimizing effort, measured as lines of code. Particle Swarm Optimization (PSO) is employed to optimize the scoring function with respect to effort-aware metrics PofB@20%, Initial False Alarms (IFA), and Popt, ensuring early defect detection and near-ideal module ranking. After the initial ranking, a defect-aware re-ranking strategy adjusts the top 20% of LOC modules by replacing them with better candidates from the remaining modules, provided that doing so improves defect coverage without exceeding the LOC budget. Experimental results demonstrate that the proposed approach outperforms baseline methods, achieving a higher PofB@20% (0.402), a lower IFA (5.1), and a better Popt (0.756). The findings indicate that ranking modules based on predicted defects and inspection effort effectively helps testers detect more faults with reduced effort, confirming the superiority of the PSO-optimized ranking methodology over traditional approaches.