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    <abstract>To extend the scope of coding queries to more realistic settings, we propose ODEX, the first Open-Domain EXecution-based natural language (NL) to Python code generation dataset. ODEX has 945 NL-Code pairs spanning 79 diverse libraries, along with 1,707 human-written test cases for execution. Our NL-Code pairs are harvested from StackOverflow forums to encourage natural and practical coding queries. Moreover, ODEX supports four natural languages as intents, in English, Spanish, Japanese, and Russian. ODEX unveils intriguing behavioral differences among top-performing code language models (LM). While CODEX achieves better overall results, CODEGEN improves effectively via scaling – CODEGEN 6.1B performs comparably with CODEX 12B. Both models show substantial gaps between open and closed domains, but CODEGEN gaps tend to decrease with model size while CODEX gaps increase. We release ODEX to facilitate research into open-domain problems for the code generation community.</abstract>
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%0 Conference Proceedings
%T Execution-Based Evaluation for Open-Domain Code Generation
%A Wang, Zhiruo
%A Zhou, Shuyan
%A Fried, Daniel
%A Neubig, Graham
%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 wang-etal-2023-execution
%X To extend the scope of coding queries to more realistic settings, we propose ODEX, the first Open-Domain EXecution-based natural language (NL) to Python code generation dataset. ODEX has 945 NL-Code pairs spanning 79 diverse libraries, along with 1,707 human-written test cases for execution. Our NL-Code pairs are harvested from StackOverflow forums to encourage natural and practical coding queries. Moreover, ODEX supports four natural languages as intents, in English, Spanish, Japanese, and Russian. ODEX unveils intriguing behavioral differences among top-performing code language models (LM). While CODEX achieves better overall results, CODEGEN improves effectively via scaling – CODEGEN 6.1B performs comparably with CODEX 12B. Both models show substantial gaps between open and closed domains, but CODEGEN gaps tend to decrease with model size while CODEX gaps increase. We release ODEX to facilitate research into open-domain problems for the code generation community.
%R 10.18653/v1/2023.findings-emnlp.89
%U https://aclanthology.org/2023.findings-emnlp.89
%U https://doi.org/10.18653/v1/2023.findings-emnlp.89
%P 1271-1290

Markdown (Informal)

[Execution-Based Evaluation for Open-Domain Code Generation](https://aclanthology.org/2023.findings-emnlp.89) (Wang et al., Findings 2023)

• Execution-Based Evaluation for Open-Domain Code Generation (Wang et al., Findings 2023)

ACL

• Zhiruo Wang, Shuyan Zhou, Daniel Fried, and Graham Neubig. 2023. Execution-Based Evaluation for Open-Domain Code Generation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1271–1290, Singapore. Association for Computational Linguistics.