UAgent: Adversarial Co-evolution for Targeted Bug Revelation in Unit Testing

Abstract

Unit testing remains crucial for software quality assurance, yet manually creating effective tests requires significant expertise. Only 17% of 82,447 GitHub projects contain test cases, revealing low adoption rates. While automated tools like Pynguin and EvoSuite reduce effort, their dependence on code coverage metrics is problematic due to weak correlation with defect detection. Current limitations include: (1) Defects in LLM-generated tests, particularly for strongly-typed languages (2) Inadequate simulation of progressive attacks in traditional mutation testing To address these, we propose UAgent - a novel adversarial co-evolution framework with: - TG Agent (Defender): Creates test suites via reflective closed-loop iteration - MG Agent (Attacker): Generates coupled mutations exposing defensive gaps - Resilience Evolution Mechanism: Enables continuous adaptation through agent competition By transforming test generation into a dynamic adaptation process, UAgent establishes a new paradigm for resilient software validation, addressing fundamental gaps in current test automation methodologies through biologically-inspired learning mechanisms. Key contributions include: - First application of adversarial ML principles to test generation - Framework-agnostic implementation for Python/Java ecosystems.