Automated unit test generation plays a critical role in maintaining software quality, yet existing LLM-based tools often struggle with limited repository-level context and rigid generation workflows. In this paper, we present TestAgent, a multi-agent tool implemented as a VS Code extension that automates the generation of high-quality unit tests for Java projects using repository-level Code Knowledge Graphs. Unlike existing tools that rely on coarse-grained workflows, TestAgent mirrors the structured practices of human developers, who typically analyze requirements before writing tests and review results afterward, by coordinating three specialized agents: a Planner to derive testing requirements, a Generator to synthesize executable code via tool-assisted retrieval, and a Reviewer to iteratively refine tests based on execution feedback. Evaluation on 1,451 methods across six Java projects demonstrates that TestAgent achieves a 92.34% line coverage, significantly outperforming baselines like EvoSuite and ChatUniTest. Furthermore, the tool successfully detects 154 real-world bugs with high precision (92.22%), demonstrating its practical utility for regression testing and bug discovery. The tool source code and demonstration are available at: https://github.com/iSEngLab/TestAgent-VSCode-Extension.
Ye Shang, Quanjun Zhang, Zheng Zhan et al.· SIGSOFT FSE Companion· 0 citations
GUI agents have advanced rapidly, producing a growing body of frameworks, benchmarks, and applications. However, this growth has outpaced the maturity of the field. GUI agents remain technically brittle, incompletely engineered, and insufficiently validated for sustained real-world use. They are evolving into closed-loop software systems. Within these systems, model reasoning is coupled with interface perception, execution feedback, recovery, and human oversight. This evolution calls for a software engineering perspective that remains largely absent from existing research. We address this gap by reviewing 336 GUI-agent papers from January 2018 to April 2026. Five research questions examine the research landscape, architectures, evaluation, software lifecycle concerns, and future opportunities. Our findings show that the field has expanded sharply since 2024, while mobile and web settings remain dominant. Architectures increasingly adopt modular perceive-reason-act loops, but recovery, human escalation, safety enforcement, and auditability remain underdeveloped. This architectural imbalance extends to evaluation. Evaluations are becoming more interactive, but they remain centered on task success and are difficult to compare across protocols. More broadly, existing studies provide limited support for testing beyond benchmarks and for maintaining agents after release. Observability, privacy engineering, and systematic human oversight are also underdeveloped. Together, these findings show that capability improvements alone cannot ensure deployment readiness. Future research should connect dependable execution with lifecycle-centered testing and reproducible evaluation. It should also integrate permission and privacy controls with cost-aware, human-centered governance. This integration is necessary to build dependable, maintainable, secure, and deployable GUI-agent systems.
Shengcheng Yu, Yuchen Ling, Junyang Xing et al.· 0 citations