CODET: CODE GENERATION WITH GENERATED TESTS

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 pretrained 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. * The first three authors contributed equally. 1 We report the results on the HumanEval benchmark with the Codex model code-cushman-001. More results with different models and benchmarks can be found in Section 4.1 and 4.2 2 https://github.com/features/copilot 1

1. INTRODUCTION

Despite the remarkable progress in pre-training techniques for code generation, selecting a single correct solution from multiple candidates generated by large language models remains a hard problem. For instance, Codex (Chen et al., 2021) , a state-of-the-art pre-trained language model for code generation, can achieve a pass@100 (pass if one or more among 100 generated solutions for a given problem can pass the corresponding test cases) of 77.4%, but a pass@1 (correct rate of a single solution) of only 33.5% on the HumanEval benchmark (Chen et al., 2021) 1 . This huge gap limits the practical usefulness of code generation models and motivates us to explore how to pick the correct or best solution from multiple candidates. A straightforward way to verify the correctness of a solution is to execute it and check if it passes all corresponding test cases. This execution-guided approach has been widely adopted in various code-related tasks, such as code generation (Chen et al., 2021; Li et al., 2022b; Shi et al., 2022) , code translation (Roziere et al., 2021), and program synthesis (Chen et al., 2018; Ellis et al., 2019) . However, this approach relies heavily on the quality and quantity of test cases, which are often costly and time-consuming to create and maintain. Moreover, in real-world applications like Copilot 2 , a code generation tool that assists developers in writing code, it is unrealistic to expect users to provide test cases for every problem they want to solve. Therefore, we propose to automatically generate test cases for arbitrary programming problems and use them to quickly verify any solution.

