Paper published in a book (Scientific congresses, symposiums and conference proceedings)
Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair
Tian, Haoye; Liu, Kui; Kabore, Abdoul Kader et al.
2020In Tian, Haoye (Ed.) 35th IEEE/ACM International Conference on Automated Software Engineering, September 21-25, 2020, Melbourne, Australia
Peer reviewed
 

Files


Full Text
evaluating represetation learning of code changes for predicting patch correctness in program repair.pdf
Author preprint (2.05 MB)
Download

All documents in ORBilu are protected by a user license.

Send to



Details



Keywords :
Computer Science - Software Engineering
Abstract :
[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...
Disciplines :
Computer science
Author, co-author :
Tian, Haoye ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Liu, Kui ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Kabore, Abdoul Kader  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Koyuncu, Anil ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Li, Li
Klein, Jacques ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Bissyande, Tegawendé François D Assise  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
External co-authors :
yes
Language :
English
Title :
Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair
Publication date :
2020
Event name :
35th IEEE/ACM International Conference on Automated Software Engineering
Event date :
21-09-220 to 25-09-2020
Main work title :
35th IEEE/ACM International Conference on Automated Software Engineering, September 21-25, 2020, Melbourne, Australia
Author, co-author :
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
Available on ORBilu :
since 14 January 2021

Statistics


Number of views
154 (69 by Unilu)
Number of downloads
78 (16 by Unilu)

Scopus citations®
 
62
Scopus citations®
without self-citations
50
OpenCitations
 
17
WoS citations
 
61

Bibliography


Similar publications



Contact ORBilu