Article (Scientific journals)
The Best of Both Worlds: Combining Learned Embeddings with Engineered Features for Accurate Prediction of Correct Patches
Tian, Haoye; Liu, Kui; Li, Yinghua et al.
2022In ACM Transactions on Software Engineering and Methodology
Peer reviewed
 

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Keywords :
Program repair; Patch overfitting; Patch correctness
Disciplines :
Computer science
Author, co-author :
Tian, Haoye ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Liu, Kui;  Huawei
Li, Yinghua  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
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
Habib, Andrew ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Li, Li;  Monash University
Wen, Junhao;  Chongqing University
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 :
The Best of Both Worlds: Combining Learned Embeddings with Engineered Features for Accurate Prediction of Correct Patches
Publication date :
2022
Journal title :
ACM Transactions on Software Engineering and Methodology
Publisher :
Association for Computing Machinery (ACM), United States
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
European Projects :
H2020 - 949014 - NATURAL - Natural Program Repair
Funders :
CE - Commission Européenne [BE]
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