Developers frequently encounter the issue of AI-generated code not working as expected. AI language models can produce code snippets, but these often require multiple rounds of debugging and refinement. This slows down the development, making the process time-consuming.
Traditional tools and methods offer some relief but aren’t fully effective. IDEs provide code suggestions and highlight errors, while automated testing frameworks help identify issues. Yet, these solutions still demand considerable manual effort to tweak and perfect the generated code.
Meet Micro Agent, a new tool designed to tackle this problem head-on. It automates both the generation of code and the iterative process of fixing it. Developers can point Micro Agent at a specific file, and a test case (or a design screenshot), and the tool will repeatedly generate and refine the code until it meets the required criteria. This eliminates the need for developers to intervene manually in each iteration.
Here’s how it works: Micro Agent runs a specified test script after each code generation attempt. If the code doesn’t pass the test, the tool modifies it and runs the test again. This process continues until the code passes all tests or matches the design screenshot. For example, if one needs to fix a TypeScript file, Micro Agent will keep updating the file and testing it until all tests pass. There’s also an experimental feature for visual matching, where the tool adjusts the code to align with a provided design screenshot.
Micro Agent attempts up to 10 iterations by default, which can be adjusted according to the developer’s needs. The tool supports different AI models like GPT-4 and GPT-3.5-turbo for various tasks. For visual matching, it integrates with Figma, ensuring precise design-to-code conversion. This multi-agent approach combines visual feedback with code generation, enhancing the tool’s accuracy and efficiency.
Micro Agent offers a practical solution for improving the reliability and efficiency of AI-generated code. By automating the debugging and refinement process, it helps developers achieve functional code more quickly and with less manual effort. While it isn’t a comprehensive development tool, its focused capabilities make it a valuable asset for developers looking to streamline their coding and testing workflows.
Niharika is a Technical consulting intern at Marktechpost. She is a third year undergraduate, currently pursuing her B.Tech from Indian Institute of Technology(IIT), Kharagpur. She is a highly enthusiastic individual with a keen interest in Machine learning, Data science and AI and an avid reader of the latest developments in these fields.
