Code Agents are State of The Art Software Testers

Niels Mündler, Mark Niklas Mueller, Jingxuan He, Martin Vechev

Keywords: language model, test generation, code agent, code repair

TL;DR: We propose a new test generation benchmark and show that LLM based Code Agents excel at this task, outperforming prior methods.

Abstract: Rigorous software testing is crucial for developing and maintaining high-quality code, making automated test generation a promising avenue for both improving software quality and boosting the effectiveness of code generation methods. However, while code generation with Large Language Models (LLMs) is an extraordinarily active research area, test generation remains relatively unexplored. We address this gap and investigate the capability of LLM-based Code Agents for formalizing user issues into test cases. To this end, we propose a novel benchmark based on popular GitHub repositories, containing real-world issues, ground-truth patches, and golden tests. We find that LLMs generally perform surprisingly well at generating relevant test cases with Code Agents designed for code repair, exceeding the performance of systems designed specifically for test generation. Finally, we find that generated tests are an effective filter for proposed code fixes, doubling the precision of SWE-Agent.

Submission Number: 19