基于细粒度代码表示和特征融合的即时软件缺陷预测方法

doi:10.11896/jsjkx.240200046

Abstract

Abstract: Just-in-time software defect prediction(JIT-SDP) aims to predict the defect tendency of software changes at the time when they are first committed.Such predictions are made on a single program change rather than on a coarse granularity.It has been widely used in fields such as continuous testing due to its immediacy and traceability.Existing JIT-SDP studies extract features from code changes at a coarse granularity,merely marking the changed lines without fine-grained tagging.Moreover,studies based on commit content are limited to simple concatenation of features extracted from commit messages and code changes,lacking deep alignment in feature space.This makes the prediction results tend to be disturbed by noise when the quality of committed message cannot be guaranteed.Existing methods also fail to fully utilize artificial features designed by domain experts and semantic syntax structure information in commit content at the same time,thus not fully leveraging existing features.To address these problems,a JIT-SDP approach based on fine-grained code changes and feature fusion is proposed.The method introduces new change embeddings to represent code changes at a fine granularity.By designing a feature alignment module,the impact of noise in low-quality commit message on performance is reduced.Meanwhile,neural networks are used to learn domain-specific knowledge from artificial features and fully utilize existing features.Experimental results show that compared to existing me-thods,this approach improves significantly on three performance metrics.

Key words: Just-in-time software defect prediction, Feature fusion, Software engineering, Deep learning, Code representation

CLC Number:

TP311

ZHU Xiaoyan, WANG Wenge, WANG Jiayin, ZHANG Xuanping. Just-In-Time Software Defect Prediction Approach Based on Fine-grained Code Representationand Feature Fusion[J].Computer Science, 2025, 52(1): 242-249.

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Just-In-Time Software Defect Prediction Approach Based on Fine-grained Code Representationand Feature Fusion

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