Test vs Mutant: Adversarial LLM Agents for Robust Unit Test Generation

• URL: http://arxiv.org/abs/2602.08146v2
• Date: Tue, 10 Feb 2026 04:06:28 GMT
• Title: Test vs Mutant: Adversarial LLM Agents for Robust Unit Test Generation
• Authors: Pengyu Chang, Yixiong Fang, Silin Chen, Yuling Shi, Beijun Shen, Xiaodong Gu,
• Abstract summary: Large language model (LLM)-based methods generate more human-readable tests but often suffer from low coverage and compilability.<n>We propose AdverTest, a novel adversarial framework for LLM-powered test case generation.<n>We show that our approach improves fault detection rates by 8.56% over the best existing LLM-based methods and by 63.30% over EvoSuite.
• Score: 9.439427795905637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software testing is a critical, yet resource-intensive phase of the software development lifecycle. Over the years, various automated tools have been developed to aid in this process. Search-based approaches typically achieve high coverage but produce tests with low readability, whereas large language model (LLM)-based methods generate more human-readable tests but often suffer from low coverage and compilability. While the majority of research efforts have focused on improving test coverage and readability, little attention has been paid to enhancing the robustness of bug detection, particularly in exposing corner cases and vulnerable execution paths. To address this gap, we propose AdverTest, a novel adversarial framework for LLM-powered test case generation. AdverTest comprises two interacting agents: a test case generation agent (T) and a mutant generation agent (M). These agents engage in an adversarial loop, where M persistently creates new mutants "hacking" the blind spots of T's current test suite, while T iteratively refines its test cases to "kill" the challenging mutants produced by M. This interaction loop is guided by both coverage and mutation scores, enabling the system to co-evolve toward both high test coverage and bug detection capability. Experimental results in the Defects4J dataset show that our approach improves fault detection rates by 8.56% over the best existing LLM-based methods and by 63.30% over EvoSuite, while also improving line and branch coverage.

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