Ahmed Abdu1, Zhengjun Zhai1,2, Hakim A. Abdo3, Redhwan Algabri4,*, Sungon Lee5,*
CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 161-180, 2023, DOI:10.32604/cmc.2023.043680
Abstract Cross-project software defect prediction (CPDP) aims to enhance defect prediction in target projects with limited
or no historical data by leveraging information from related source projects. The existing CPDP approaches rely
on static metrics or dynamic syntactic features, which have shown limited effectiveness in CPDP due to their
inability to capture higher-level system properties, such as complex design patterns, relationships between multiple
functions, and dependencies in different software projects, that are important for CPDP. This paper introduces
a novel approach, a graph-based feature learning model for CPDP (GB-CPDP), that utilizes NetworkX to extract
features and… More >
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