Automated Program Repair Based on REST API Specifications Using Large Language Models

TL;DR

This paper introduces dcFix, a novel approach that uses large language models to automatically detect and repair REST API misuses in client code, improving debugging efficiency and accuracy.

Contribution

It presents a new method combining API specifications with LLMs to detect and fix REST API misuses automatically, outperforming baseline prompt strategies.

Findings

dcFix accurately detects REST API misuses

Outperforms baseline prompt-based approaches

Demonstrates effective automatic code repair

Abstract

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.