Network Embedding Techniques for Predicting Software Defects: A Review
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In the software development process, ensuring the quality of the software is essential. Software defect prediction (SDP) is of significant importance in identifying software modules with a high likelihood of defects. Several machine learning-based defect prediction models have been developed and implemented in recent years. Researchers have also utilized network embedding for SDP, showcasing the adaptability of Natural Language Processing techniques within the domain of defect prediction. This study aims to review, investigate, and discuss network embedding's use in SDP. We examined the previous 15 years' defect prediction articles using network embedding, the majority of which were published in notable conferences and software engineering journals. Each network embedding technique, its findings, and its particular roles in SDP have been described in detail. The papers that have been reviewed are listed in the order of publication along with their comparative assessment. We have developed three research questions that emphasize the significance of analyzing network representations, particularly network embedding, for identifying potential software defects. According to our knowledge, this review is the first to include a thorough analysis of both the transductive and inductive variants of network embedding, along with their potential in machine learning (ML) for predicting software defects. This article extensively explores the challenges and puts forth potential research directions as solutions, intending to effectively guide future research efforts for academics and practitioners in the field of SDP.
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