QAagent: A Multiagent System for Unit Test Generation via Natural Language Pseudocode (Student Abstract)

摘要

Unit testing is essential for ensuring software quality, but it is often time-consuming and prone to developer oversight. With the rise of large language models (LLMs) in code generation, there is an increasing need for reliable and automated test generation systems. This work presents QAagent, a multi-agent system designed to generate unit tests using natural language pseudocode. QAagent leverages LLMs to create a detailed natural language plan of a function's implementation and then generates a comprehensive suite of test cases covering both base and edge scenarios. Experiments conducted on two widely-used benchmarks, HumanEval and MBPP, show that QAagent consistently outperforms existing frameworks in terms of code coverage, although its accuracy varies across datasets, demonstrating the potential for utilizing natural language pseudocode to to enhance automated test generation in LLM-driven coding environments.