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Background: The early stage of defect prediction in the software developmentlife cycle can reduce testing effort and ensure the quality of software. Due tothe lack of historical data within the same project, Cross-Project DefectPrediction (CPDP) has become a popular research topic among researchers. CPDPtrained classifiers based on labeled data sets of one project to predict faultin another project. Goals: Software Defect Prediction (SDP) data sets consistof manually designed static features, which are software metrics. In CPDP,source and target project data divergence is the major challenge in achievinghigh performance. In this paper, we propose a Generative Adversarial Network(GAN)-based data transformation to reduce data divergence between source andtarget projects. Method: We apply the Generative Adversarial Method where labeldata sets are choosing as real data, while target data sets are choosing asfake data. The Discriminator tries to measure the perfection of domainadaptation through loss function. Through the generator, target data sets tryto adapt the source project domain and, finally, apply machine learningclassifier (i.e., Naive Bayes) to classify faulty modules. Results: Our resultshows that it is possible to predict defects based on the GenerativeAdversarial Method. Our model performs quite well in a cross-projectenvironment when we choose JDT as a target data sets. However, all chosen datasets are facing a large class imbalance problem which affects the performanceof our model.

Generative Adversarial Network-based Cross-Project Fault Prediction