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- W4378373692 abstract "Deep learning-based program repair has received significant attention from the research community lately. Most existing techniques treat source code as a sequence of tokens or abstract syntax trees. Consequently, they cannot incorporate semantic contextual information pertaining to a buggy line of code and its fix. In this work, we propose a program repair technique called GLANCE that combines static program analysis with graph-to-sequence learning for capturing contextual information. To represent contextual information, we introduce a graph representation that can encode information about the buggy code and its repair ingredients by embedding control and data flow information. We employ a fine-grained graphical code representation, which explicitly describes code change context and embeds semantic relationships between code elements. GLANCE leverages a graph neural network and a sequence-based decoder to learn from this semantic code representation. We evaluated our work against six state-of-the-art techniques, and our results show that GLANCE fixes 52% more bugs than the best performing technique." @default.
- W4378373692 created "2023-05-27" @default.
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- W4378373692 date "2023-04-01" @default.
- W4378373692 modified "2023-10-16" @default.
- W4378373692 title "Embedding Context as Code Dependencies for Neural Program Repair" @default.
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- W4378373692 doi "https://doi.org/10.1109/icst57152.2023.00018" @default.
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