Low‐rank representation for semi‐supervised software defect prediction

Abstract

Software defect prediction based on machine learning is an active research topic in the field of software engineering. The historical defect data in software repositories may contain noises because automatic defect collection is based on modified logs and defect reports. When the previous defect labels of modules are limited, predicting the defect‐prone modules becomes a challenging problem. In this study, the authors propose a graph‐based semi‐supervised defect prediction approach to solve the problems of insufficient labelled data and noisy data. Graph‐based semi‐supervised learning methods used the labelled and unlabelled data simultaneously and consider them as the nodes of the graph at the training phase. Therefore, they solve the problem of insufficient labelled samples. To improve the stability of noisy defect data, a powerful clustering method, low‐rank representation (LRR), and neighbourhood distance are used to construct the relationship graph of samples. Therefore, they propose a new semi‐supervised defect prediction approach, named low‐rank representation‐based semi‐supervised software defect prediction (LRRSSDP). The widely used datasets from NASA projects and noisy datasets are employed as test data to evaluate the performance. Experimental results show that (i) LRRSSDP outperforms several representative state‐of‐the‐art semi‐supervised defect prediction methods; and (ii) LRRSSDP can maintain robustness in noisy environments.