Cross project defect prediction: a comprehensive survey with its SWOT analysis

Abstract

Software fault prediction (SFP) refers to the process of identifying (or predicting) faulty modules based on its characteristics/software metrics. SFP can be done either using the same project data in both the training and testing phase i.e. within project defect prediction or using a different one, as done in cross-project defect prediction (CPDP). Previous works show that contemporary research in this field is progressing towards CPDP. To present the current state of progress and the future prospects of CPDP, this article presents a comprehensive survey of CPDP considering the latest work along with its SWOT analysis. This survey is targeted to present the novice researchers, academicians, and practitioners with the alphas and omegas of this contemporary challenging field. We have also carried a qualitative and quantitative evaluation of CPDP w.r.t some of the targeted research questions. A total of 34 significant primary CPDP studies published from 2008 to 2019 were selected. Both qualitative and quantitative data are extracted from each study. The collected data is then consolidated and analyzed to present a comprehensive report showing the current state of the art, along with the answers to the targeted research questions and finally the CPDP SWOT analysis. We observed that there exists a big scope for performance improvement in CPDP. Integration of feature engineering, exploration with different process metrics, hyperparameter tuning, class imbalance handling in CPDP setting are some of the ways identified for bringing enhancement in CPDP performance. Apart from this, we would like to conclude that there is a strong need to investigate Precision over the Recall and model’s validity in terms of effort/cost-effectiveness.