CodeT: Code Generation with Generated Tests

TL;DR

CodeT automatically generates test cases using pre-trained models to evaluate and select the best code solutions, significantly improving code correctness and selection accuracy across multiple benchmarks.

Contribution

The paper introduces CodeT, a novel method that leverages pre-trained language models to generate test cases for code solutions, reducing manual effort and enhancing selection performance.

Findings

CodeT improves pass@1 metric on HumanEval to 65.8%.

CodeT achieves over 20% absolute improvement over previous state-of-the-art.

CodeT demonstrates consistent gains across multiple benchmarks and models.

Abstract

The task of generating code solutions for a given programming problem can benefit from the use of pre-trained language models such as Codex, which can produce multiple diverse samples. However, a major challenge for this task is to select the most appropriate solution from the multiple samples generated by the pre-trained language models. A natural way to evaluate the quality and correctness of a code solution is to run it against a set of test cases, but the manual creation of such test cases is often costly and time-consuming. In this paper, we propose a novel method, CodeT, that leverages the same pre-trained language models to automatically generate test cases for the code samples, thus reducing the human effort and increasing the coverage of the test scenarios. CodeT then executes the code samples using the generated test cases, and performs a dual execution agreement, which considers both the consistency of the outputs against the generated test cases and the agreement of the outputs with other code samples. We conduct comprehensive experiments on four benchmarks, HumanEval, MBPP, APPS and CodeContests, using five different pre-trained language models with varying sizes and capabilities. Our results show that CodeT can significantly improve the performance of code solution selection over previous methods, achieving remarkable and consistent gains across different models and benchmarks. For instance, CodeT improves the pass@1 metric on HumanEval to 65.8%, which represents an absolute improvement of 18.8% over the code-davinci-002 model, and an absolute improvement of more than 20% over the previous state-of-the-art results.