Fight Fire With Fire: How Much Can We Trust ChatGPT on Source Code-Related Tasks?
Abstract
With the increasing utilization of large language models such as ChatGPT during software development, it has become crucial to verify the quality of code content it generates. Recent studies proposed utilizing ChatGPT as both a developer and tester for multi-agent collaborative software development. The multi-agent collaboration empowers ChatGPT to produce test reports for its generated code, enabling it to self-verify the code content and fix bugs based on these reports. However, these studies did not assess the effectiveness of the generated test reports in validating the code. Therefore, we conduct a comprehensive empirical investigation to evaluate ChatGPT's self-verification capability in code generation, code completion, and program repair. We request ChatGPT to (1) generate correct code and then self-verify its correctness; (2) complete code without vulnerabilities and then self-verify for the presence of vulnerabilities; and (3) repair buggy code and then selfverify whether the bugs are resolved. Our findings on two code generation datasets, one code completion dataset, and two program repair datasets reveal the following observations: (1) ChatGPT often erroneously predicts its generated incorrect code as correct, its vulnerable completed code as non-vulnerable, and its failed program repairs as successful during its self-verification. (2) The self-contradictory hallucinations in ChatGPT's behavior arise: (a) ChatGPT initially generates code that it believes to be correct but later predicts it to be incorrect; (b) ChatGPT initially generates code completions that it deems secure but later predicts them to be vulnerable; (c) ChatGPT initially outputs code that it considers successfully repaired but later predicts it to be buggy during its self-verification. (3) The self-verification Received 20 April 2024; revised 21 October 2024; accepted 27 October 2024. Date of publication 5 November 2024; date of current version 12 December 2024. This work was supported in part by the National Natural Science Foundation of China under Grant 61972290, in part by Ningbo Natural Science Foundation under Grant 2023J292, and in part by the General Research Fund of the Re