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- W4387397652 abstract "Abstract Software fault localization is commonly recognized as arduous and time consuming. Spectrum‐based fault localization (SBFL) has been widely used due to its lightness. However, the effectiveness of SBFL is limited since it only considers simple statistics on the coverage information, ignoring the tie problem that the spectrum matrixes of some statements are the same. Most existing deep learning‐based fault localization (DLFL) techniques convert the coverage information into a vector, which utilizes the spectrum in a simplified manner and still has limitations in practice. To solve the above problem, we propose an approach via the weighted execution graph and graph attention network (WEGAT). We use a graph structure to represent the coverage information between test cases and program elements. Then, we generate a weighted execution graph by applying the predicate execution sequence. Furthermore, we combine the weighted execution graph with the AST as an integrated graph, which is the input of the GAT for fault localization. We evaluate WEGAT in within‐project and cross‐project prediction scenarios on the Defects4J benchmark. Experimental results show that our approach outperforms traditional SBFL (Ochiai, DStar and Tarantula) and DLFL (TraPT, CNN‐FL, Grace, and AGFL) methods, effectively improving the accuracy of fault localization." @default.
- W4387397652 created "2023-10-07" @default.
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- W4387397652 date "2023-10-05" @default.
- W4387397652 modified "2023-10-07" @default.
- W4387397652 title "Improving fault localization via weighted execution graph and graph attention network" @default.
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- W4387397652 doi "https://doi.org/10.1002/smr.2619" @default.
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