Xin Fan1,2, Zhenlei Fu1,2,*, Jian Shu1,2, Zuxiong Shen1,2, Yun Ge1,2
CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2583-2607, 2025, DOI:10.32604/cmc.2024.057695
- 17 February 2025
Abstract Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of… More >
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