Generative Adversarial Networks-Based Imbalance Learning in Software Aging-Related Bug Prediction

Abstract

Software aging refers to a problem of performance decay in the software systems, which are running for a long period. The primary cause of this phenomenon is the accumulation of run-time errors in the software, which are also known as aging-related bugs (ARBs). Many efforts have been reported earlier to predict the origin of ARBs in the software so that these bugs can be identified and fixed during testing. Imbalanced dataset, where the representation of ARBs patterns is very less as compared to the representation of the non-ARBs pattern significantly hinders the performance of the ARBs prediction models. Therefore, in this article, we present an oversampling approach, generative adversarial networks-based synthetic data generation-based ARBs prediction models. The approach uses generative adversarial networks to generate synthetic samples for the ARBs patterns in the given datasets implicitly and build the prediction models on the processed datasets. To validate the performance of the presented approach, we perform an experimental study for the seven ARBs datasets collected from the public repository and use various performance measures to evaluate the results. The experimental results showed that the presented approach led to the improved performance of prediction models for the ARBs prediction as compared to the other state-of-the-art models.