A Selection Technique of Source Project in Heterogeneous Defect Prediction based on Correlation Coefficients

Eunseob Kim, Jongmoon Baik, Duksan Ryu

http://doi.org/10.5626/JOK.2021.48.8.920

The software defect prediction techniques try to predict defect-prone modules and ensure the quality of the developing software using previous defect data. Nowadays, heterogeneous defect prediction (HDP) techniques have been applying defect prediction techniques even when the metrics between source and target projects are different. Previous HDP techniques focused on improving prediction performance when the source and target projects were given. However in a real development environment, more than one source projects exist for one target project, thus identifying a project that is suitable for source data is challenging. This paper suggests a correlation-based selection technique for source projects in HDP. After the metric matching process, correlation coefficients are calculated for each corresponding metric, and the project with the highest score is selected for source data. The experiment shows that the performance of the proposed selection method is higher than the results of random selection, and removing projects with less than 100 instances from the source candidates improves the performance. Therefore, using the proposed selection technique could improve the prediction accuracy in HDP.