How Far We Have Progressed in the Journey? An Examination of Cross-Project Defect Prediction

Abstract

Background. Recent years have seen an increasing interest in cross-project defect prediction (CPDP), which aims to apply defect prediction models built on source projects to a target project. Currently, a variety of (complex) CPDP models have been proposed with a promising prediction performance. Problem. Most, if not all, of the existing CPDP models are not compared against those simple module size models that are easy to implement and have shown a good performance in defect prediction in the literature. Objective. We aim to investigate how far we have really progressed in the journey by comparing the performance in defect prediction between the existing CPDP models and simple module size models. Method. We first use module size in the target project to build two simple defect prediction models, ManualDown and ManualUp, which do not require any training data from source projects. ManualDown considers a larger module as more defect-prone, while ManualUp considers a smaller module as more defect-prone. Then, we take the following measures to ensure a fair comparison on the performance in defect prediction between the existing CPDP models and the simple module size models: using the same publicly available data sets, using the same performance indicators, and using the prediction performance reported in the original cross-project defect prediction studies. Result. The simple module size models have a prediction performance comparable or even superior to most of the existing CPDP models in the literature, including many newly proposed models. Conclusion. The results caution us that, if the prediction performance is the goal, the real progress in CPDP is not being achieved as it might have been envisaged. We hence recommend that future studies should include ManualDown/ManualUp as the baseline models for comparison when developing new CPDP models to predict defects in a complete target project.