EANDL: A High-performance and Low-cost Effort-Aware Just-in-Time Software Defect Prediction Model

Abstract Just-in-time software defect prediction (JIT-SDP) is a fine-grained software defect prediction technology and its predictive entity is code changes. Effort-aware just-in-time software defect prediction (Effort-aware JIT-SDP) is a defect prediction technology that takes the cost of detection into consideration with the goal of detecting more defect changes with less cost. To solve the problem of poor prediction performance of Effort aware JIT-SDP model, an Effort-aware JIT-SDP model called EANDL based on NSGA3, Differential Evolution and Logistic Regression is proposed. Firstly, two evaluation metrics called DPR and LDSOD are proposed to evaluate the sorting performance. Secondly, the prediction model is constructed by using logistic regression and maximizing two evaluation metrics using NSGA3-DE so as to determine the optimal coefficient vector of logistic regression. In addition, two metrics called ACE and ACEP to measure the effort consumption of Effort-aware JIT-SDP models are proposed. Finally, intensive simulation experiments have been conducted on six open-source datasets to demonstrate the performance of the proposed method. Empirical results demonstrate that EANDL outperforms eight typical and classical supervised and unsupervised models in terms of sorting performance, effort consumption, and response time.