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Computer Science > Software Engineering

arXiv:2212.10481 (cs)
[Submitted on 20 Dec 2022 (v1), last revised 19 May 2023 (this version, v2)]

Title:Execution-Based Evaluation for Open-Domain Code Generation

Authors:Zhiruo Wang, Shuyan Zhou, Daniel Fried, Graham Neubig
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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.
Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2212.10481 [cs.SE]
  (or arXiv:2212.10481v2 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2212.10481

Submission history

From: Zhiruo Wang [view email]
[v1] Tue, 20 Dec 2022 17:54:37 UTC (3,713 KB)
[v2] Fri, 19 May 2023 14:27:46 UTC (4,241 KB)
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