research-article

CoCoNuT: combining context-aware neural translation models using ensemble for program repair

Authors:

Thibaud Lutellier,

Hung Viet Pham,

Lin TanAuthors Info & Claims

ISSTA 2020: Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis

Pages 101 - 114

https://doi.org/10.1145/3395363.3397369

Published: 18 July 2020 Publication History

Abstract

Automated generate-and-validate (GV) program repair techniques (APR) typically rely on hard-coded rules, thus only fixing bugs following specific fix patterns. These rules require a significant amount of manual effort to discover and it is hard to adapt these rules to different programming languages.

To address these challenges, we propose a new G&V technique—CoCoNuT, which uses ensemble learning on the combination of convolutional neural networks (CNNs) and a new context-aware neural machine translation (NMT) architecture to automatically fix bugs in multiple programming languages. To better represent the context of a bug, we introduce a new context-aware NMT architecture that represents the buggy source code and its surrounding context separately. CoCoNuT uses CNNs instead of recurrent neural networks (RNNs), since CNN layers can be stacked to extract hierarchical features and better model source code at different granularity levels (e.g., statements and functions). In addition, CoCoNuT takes advantage of the randomness in hyperparameter tuning to build multiple models that fix different bugs and combines these models using ensemble learning to fix more bugs.

Our evaluation on six popular benchmarks for four programming languages (Java, C, Python, and JavaScript) shows that CoCoNuT correctly fixes (i.e., the first generated patch is semantically equivalent to the developer’s patch) 509 bugs, including 309 bugs that are fixed by none of the 27 techniques with which we compare.

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Index Terms

CoCoNuT: combining context-aware neural translation models using ensemble for program repair
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Empirical software validation
      2. Software defect analysis
        Software testing and debugging

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ISSTA 2020: Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis

July 2020

591 pages

ISBN:9781450380089

DOI:10.1145/3395363

General Chair:
Sarfraz Khurshid
University of Texas at Austin, USA
,
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Corina S. Păsăreanu
Carnegie Mellon University Silicon Valley / NASA Ames Research Center, USA

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Hayet IScott Ad'Amorim M(2025)ChatAssert: LLM-Based Test Oracle Generation With External Tools AssistanceIEEE Transactions on Software Engineering10.1109/TSE.2024.351915951:1(305-319)Online publication date: Jan-2025
https://doi.org/10.1109/TSE.2024.3519159
Csuvik VHorváth DLajkó MVidács L(2025)GenProgJS: A Baseline System for Test-Based Automated Repair of JavaScript ProgramsIEEE Transactions on Software Engineering10.1109/TSE.2024.349779851:2(325-343)Online publication date: Feb-2025
https://doi.org/10.1109/TSE.2024.3497798
Wang JPham HLi QTan LGuo YAziz AMeijer E(2025)D³: Differential Testing of Distributed Deep Learning With Model GenerationIEEE Transactions on Software Engineering10.1109/TSE.2024.346165751:1(38-52)Online publication date: 1-Jan-2025
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Kim JAbdinabiev ALee B(2025)Improving Patch Optimization With Buggy Block for Complex Program RepairIEEE Access10.1109/ACCESS.2025.354133613(30420-30443)Online publication date: 2025
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