Cross-Project setting using Deep learning Architectures in Just-In-Time Software Fault Prediction: An Investigation

Abstract

The prediction of whether a software change is fault-inducing or not in the software system using various learning methods, the study concerned in Just-In-Time Software Fault Prediction (JIT-SFP). Building such predicting model requires adequate training data. However, there needs to be more training data at the beginning of the software system. Cross-Project (CP) setting can subjugate this challenge by employing data from different software projects. It can achieve similar predictive performance to Within-Project (WP) fault prediction. It is still being determined to what level the CP training data can be useful in such a situation. Furthermore, it also needs to be discovered whether CP data are helpful in the initial phase of fault detection, and when there is an inadequate WP train set, CP could be beneficial to extend. This article deals with such investigations in real software projects. We proposed a new method by levering a deep belief network and long short-term memory called JITCP-Predictor. Out of ten, the proposed model significantly outperforms every ten project benchmark methods, and it is superior from 10.63% to 136.36% and 7.04% to 35.71% in terms of MCC and F-Measure, respectively. The mean values of MCC and F-Measure produced by JITCP-Predictor are 0.52 ± 0.021 and 0.76 ± 0.76, respectively. We also found that the proposed model is more suitable for large and moderate-size projects. The proposed model avoids class imbalance and overfitting problems and takes reasonable training costs.