Automated Program Repair Based on REST API Specifications Using Large Language Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866915585280966656 |
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| author | Yamagishi, Katsuki Yoshida, Norihiro Makihara, Erina Inoue, Katsuro |
| author_facet | Yamagishi, Katsuki Yoshida, Norihiro Makihara, Erina Inoue, Katsuro |
| contents | Many cloud services provide REST API accessible to client applications. However, developers often identify specification violations only during testing, as error messages typically lack the detail necessary for effective diagnosis. Consequently, debugging requires trial and error. This study proposes dcFix, a method for detecting and automatically repairing REST API misuses in client programs. In particular, dcFix identifies non-conforming code fragments, integrates them with the relevant API specifications into prompts, and leverages a Large Language Model (LLM) to produce the corrected code. Our evaluation demonstrates that dcFix accurately detects misuse and outperforms the baseline approach, in which prompts to the LLM omit any indication of code fragments non conforming to REST API specifications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25148 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Program Repair Based on REST API Specifications Using Large Language Models Yamagishi, Katsuki Yoshida, Norihiro Makihara, Erina Inoue, Katsuro Software Engineering Many cloud services provide REST API accessible to client applications. However, developers often identify specification violations only during testing, as error messages typically lack the detail necessary for effective diagnosis. Consequently, debugging requires trial and error. This study proposes dcFix, a method for detecting and automatically repairing REST API misuses in client programs. In particular, dcFix identifies non-conforming code fragments, integrates them with the relevant API specifications into prompts, and leverages a Large Language Model (LLM) to produce the corrected code. Our evaluation demonstrates that dcFix accurately detects misuse and outperforms the baseline approach, in which prompts to the LLM omit any indication of code fragments non conforming to REST API specifications. |
| title | Automated Program Repair Based on REST API Specifications Using Large Language Models |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2510.25148 |