Publication
Large language models in automated repair of haskell type errors
| Summary: | This paper introduces a new method of Automated Program Repair that relies on a combination of the GPT-4 Large Language Model and automatic type checking of Haskell programs. This method identifies the source of a type error and asks GPT-4 to fix that specific portion of the program. Then, QuickCheck is used to automatically generate a large set of test cases to validate whether the generated repair behaves as the correct solution. Our publicly available experiments revealed a success rate of 88.5% in normal conditions. However, more detailed testing should be performed to more accurately evaluate this form of APR. |
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| Main Authors: | Santos, Sofia |
| Other Authors: | Saraiva, João; Ribeiro, Francisco |
| Subject: | Automated program repair Large language model Fault localization Code generation Type checking Automatic testing |
| Year: | 2024 |
| Country: | Portugal |
| Document type: | conference paper |
| Access type: | open access |
| Associated institution: | Universidade do Minho |
| Language: | English |
| Origin: | RepositóriUM - Universidade do Minho |
| Summary: | This paper introduces a new method of Automated Program Repair that relies on a combination of the GPT-4 Large Language Model and automatic type checking of Haskell programs. This method identifies the source of a type error and asks GPT-4 to fix that specific portion of the program. Then, QuickCheck is used to automatically generate a large set of test cases to validate whether the generated repair behaves as the correct solution. Our publicly available experiments revealed a success rate of 88.5% in normal conditions. However, more detailed testing should be performed to more accurately evaluate this form of APR. |
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