Number of papers: 3
- Authors: Gao, Shuzheng and Gao, Cuiyun and He, Yulan and Zeng, Jichuan and Nie, Lunyiu and Xia, Xin and Lyu, Michael
- Abstract: Code summaries help developers comprehend programs and reduce their time to infer the program functionalities during software maintenance. Recent efforts resort to deep learning techniques such as sequence-to-sequence models for generating accurate code summaries, among which Transformer-based approaches have achieved promising performance. However, effectively integrating the code structure information into the Transformer is under-explored in this task domain. In this article, we propose a nov...
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- Labels: static analysis, code summarization
- Authors: Hou, Xinyi and Zhao, Yanjie and Liu, Yue and Yang, Zhou and Wang, Kailong and Li, Li and Luo, Xiapu and Lo, David and Grundy, John and Wang, Haoyu
- Abstract: Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE). Many recent publications have explored LLMs applied to various SE tasks. Nevertheless, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages. To bridge this gap, we conducted a systematic literature review (SLR) on LLM4SE, with a particular focus on understanding how LLMs can be exploited to optimize processes and...
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- Labels: survey, general coding task
The Best of Both Worlds: Combining Learned Embeddings with Engineered Features for Accurate Prediction of Correct Patches
- Authors: Tian, Haoye and Liu, Kui and Li, Yinghua and Kabor'{e}, Abdoul Kader and Koyuncu, Anil and Habib, Andrew and Li, Li and Wen, Junhao and Klein, Jacques and Bissyand'{e}, Tegawend'{e} F.
- Abstract: A large body of the literature on automated program repair develops approaches where patches are automatically generated to be validated against an oracle (e.g., a test suite). Because such an oracle can be imperfect, the generated patches, although validated by the oracle, may actually be incorrect. While the state-of-the-art explores research directions that require dynamic information or rely on manually-crafted heuristics, we study the benefit of learning code representations in order to lea...
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- Labels: code generation, program repair, empirical study