CodeT: Code Generation with Generated Tests

• URL: http://arxiv.org/abs/2207.10397v1
• Date: Thu, 21 Jul 2022 10:18:37 GMT
• Title: CodeT: Code Generation with Generated Tests
• Authors: Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen
• Abstract summary: We explore the use of pre-trained language models to automatically generate test cases. CodeT executes the code solutions using the generated test cases, and then chooses the best solution. We evaluate CodeT on five different pre-trained models with both HumanEval and MBPP benchmarks.
• Score: 49.622590050797236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given a programming problem, pre-trained language models such as Codex have demonstrated the ability to generate multiple different code solutions via sampling. However, selecting a correct or best solution from those samples still remains a challenge. While an easy way to verify the correctness of a code solution is through executing test cases, producing high-quality test cases is prohibitively expensive. In this paper, we explore the use of pre-trained language models to automatically generate test cases, calling our method CodeT: Code generation with generated Tests. CodeT executes the code solutions using the generated test cases, and then chooses the best solution based on a dual execution agreement with both the generated test cases and other generated solutions. We evaluate CodeT on five different pre-trained models with both HumanEval and MBPP benchmarks. Extensive experimental results demonstrate CodeT can achieve significant, consistent, and surprising improvements over previous methods. For example, CodeT improves the pass@1 on HumanEval to 65.8%, an increase of absolute 18.8% on the code-davinci-002 model, and an absolute 20+% improvement over previous state-of-the-art results.

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