Reference : Evaluating Representation Learning of Code Changes for Predicting Patch Correctness i...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/45494
Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair
English
Tian, Haoye mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX]
Liu, Kui mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Kaboreé, Abdoul Kader [> >]
Koyuncu, Anil mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX]
Li, Li [> >]
Klein, Jacques mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX]
Bissyandé, Tegawendé F. [> >]
2020
35th IEEE/ACM International Conference on Automated Software Engineering, September 21-25, 2020, Melbourne, Australia
Tian, Haoye mailto
Yes
35th IEEE/ACM International Conference on Automated Software Engineering
21-09-220 to 25-09-2020
[en] Computer Science - Software Engineering
[en] A large body of the literature of automated program repair develops approaches where patches are 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 explore research directions that require dynamic information or rely on manually-crafted heuristics, we study the benefit of learning code representations to learn deep features that may encode the properties of patch correctness. Our work mainly investigates different representation learning approaches for code changes to derive embeddings that are amenable to similarity computations. We report on findings based on embeddings produced by pre-trained and re-trained neural networks. Experimental results demonstrate the potential of embeddings to empower learning algorithms in reasoning about patch correctness: a machine learning predictor with BERT transformer-based embeddings...
http://hdl.handle.net/10993/45494

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