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Execution-Based Evaluation for Open-Domain Code Generation

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arxiv 2212.10481 v2 pith:5ZHVXQ5V submitted 2022-12-20 cs.SE cs.AIcs.CL

classification cs.SEcs.AIcs.CL
keywords odexcodecodegencodexgapsgenerationnaturalopen-domain
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 2 Pith papers

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