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    <abstract>One of the fundamental goals in code search is to retrieve a functionally correct code for a given natural language query. As annotating for correctness requires executing test cases (i.e. obtaining execution feedback), existing code search training datasets approximate text-code co-occurrences as positive execution feedback. However, this approximation may misalign models’ retrieval decisions from ground-truth correctness. To address such limitation, we propose Code Intervention-based Reinforcement Learning (CIRL) that perturbs training code to result in misalignment (i.e. code intervention), then tests models’ decisions and corrects them with the execution feedback by reinforcement learning. The first technical contribution of CIRL is to induce the execution feedback from perturbation, without actual execution. Secondly, CIRL introduces structural perturbations using abstract syntax trees, going beyond simple lexical changes. Experimental results on various datasets demonstrate the effectiveness of CIRL compared to conventional approaches.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.148</identifier>
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%0 Conference Proceedings
%T Intervention-Based Alignment of Code Search with Execution Feedback
%A Han, Hojae
%A Kim, Minsoo
%A Hwang, Seung-won
%A Duan, Nan
%A Lu, Shuai
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F han-etal-2023-intervention
%X One of the fundamental goals in code search is to retrieve a functionally correct code for a given natural language query. As annotating for correctness requires executing test cases (i.e. obtaining execution feedback), existing code search training datasets approximate text-code co-occurrences as positive execution feedback. However, this approximation may misalign models’ retrieval decisions from ground-truth correctness. To address such limitation, we propose Code Intervention-based Reinforcement Learning (CIRL) that perturbs training code to result in misalignment (i.e. code intervention), then tests models’ decisions and corrects them with the execution feedback by reinforcement learning. The first technical contribution of CIRL is to induce the execution feedback from perturbation, without actual execution. Secondly, CIRL introduces structural perturbations using abstract syntax trees, going beyond simple lexical changes. Experimental results on various datasets demonstrate the effectiveness of CIRL compared to conventional approaches.
%R 10.18653/v1/2023.findings-emnlp.148
%U https://aclanthology.org/2023.findings-emnlp.148
%U https://doi.org/10.18653/v1/2023.findings-emnlp.148
%P 2241-2263

Markdown (Informal)

[Intervention-Based Alignment of Code Search with Execution Feedback](https://aclanthology.org/2023.findings-emnlp.148) (Han et al., Findings 2023)

• Intervention-Based Alignment of Code Search with Execution Feedback (Han et al., Findings 2023)

ACL

• Hojae Han, Minsoo Kim, Seung-won Hwang, Nan Duan, and Shuai Lu. 2023. Intervention-Based Alignment of Code Search with Execution Feedback. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2241–2263, Singapore. Association for Computational Linguistics.