| Challenge: | Existing work performs code repair and commit message generation independently. |
| Approach: | They propose a cascaded method to repair program codes and generate commit messages in a unified framework. |
| Outcome: | The proposed model significantly outperforms baselines on a buggy-fixed-commit dataset. |
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| Challenge: | Existing approaches fix a single error in a line, but it is inevitable to iterate until no errors remain. |
| Approach: | They propose a sequence-to-sequence learning framework for fixing multiple program errors at once . they pare an erroneous program with an optimal alignment to the correct program . |
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Exploring Data Augmentation for Code Generation Tasks (2023.findings-eacl)
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| Challenge: | Recent advances in natural language processing have impacted how models are trained for programming language tasks. |
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CascadeFix: Multi-Location Program Repair via Cascading Planning and Generation (2026.findings-acl)
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| Challenge: | Existing methods for automating program repair face insufficient bug dependency modeling and inadequate global repair planning when addressing semantically complex multi-location bugs. |
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Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5 (2022.findings-emnlp)
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The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation (2023.findings-emnlp)
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| Challenge: | Open-source dataset of code-text pairs for training large language models to understand code is outperforms other datasets for code generation and understanding tasks. |
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Leveraging Context-Aware Prompting for Commit Message Generation (2024.emnlp-main)
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| Challenge: | Existing methods for writing comprehensive commit messages focus on the changed lines or nearest context lines, but excessive contexts can lead to noise. |
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A Simple Recipe for Multilingual Grammatical Error Correction (2021.acl-short)
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Using Developer Discussions to Guide Fixing Bugs in Software (2022.findings-emnlp)
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Learning to repair: Repairing model output errors after deployment using a dynamic memory of feedback (2022.findings-naacl)
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| Challenge: | Our approach pairs an LM with a growing memory of cases where the user identified an output error and provided general feedback on how to correct it. |
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StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback (2024.acl-long)
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Shihan Dou, Yan Liu, Haoxiang Jia, Enyu Zhou, Limao Xiong, Junjie Shan, Caishuang Huang, Xiao Wang, Xiaoran Fan, Zhiheng Xi, Yuhao Zhou, Tao Ji, Rui Zheng, Qi Zhang, Tao Gui, Xuanjing Huang
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