Evaluating network embedding techniques’ performances in software bug prediction

Abstract

Software bug prediction techniques can be very helpful in testing and code inspection. Over the past decade, network measures have been successfully used in bug prediction. Following the same intuition, recently, researchers started using network embedding techniques in bug prediction. However, existing studies only evaluated the Skip-gram and CBOW models with random walk. Considering network embedding is a fast-developing research direction, it is important to evaluate other network embedding techniques’ performances in bug prediction. Moreover, existing studies have not investigated the application and performance of network embedding in effort-aware bug prediction, which is thought to be a more realistic scenario that evaluates the cost effectiveness of bug prediction models. In this paper, we conduct an extensive empirical study to evaluate network embedding algorithms in bug prediction by utilizing and extending node2defect, a newly proposed bug prediction model that combines the embedded vectors with traditional software engineering metrics through concatenation. Experiments are conducted based on seven network embedding algorithms, two effort-aware models, and 13 open-source Java systems. Experimental results show that node2defect outperforms traditional metrics by + 14.64% in terms of MCC score, and by + 7.51% to + 16.57% in effort-aware bug prediction. More interestingly, when combined with CBS + , the embedded vectors alone can achieve the best performance. Among different network embedding algorithms, the newly proposed algorithm ProNE has the best performance.