# ResTest: Targeted Coverage of Residual Not-Covered Code Using Large Language Models for Web GUI Testing

arXiv: https://arxiv.org/abs/2506.00520 · HTML: https://mlsanity.com/paper/2506.00520/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2025.

## Built on by (most cited first, 7 of 7)

- [The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration](https://mlsanity.com/paper/2603.22862/) (2026)
- [AI Agents for Web Testing: A Case Study in the Wild](https://mlsanity.com/paper/2509.05197/) (2025)
- [GUITester: Enabling GUI Agents for Exploratory Defect Discovery](https://mlsanity.com/paper/2601.04500/) (2026)
- [WebTestPilot: Agentic End-to-End Web Testing against Natural Language Specification by Inferring Oracles with Symbolized GUI Elements](https://mlsanity.com/paper/2602.11724/) (2026)
- [Agent+P: Guiding UI Agents via Symbolic Planning](https://mlsanity.com/paper/2510.06042/) (2025)
- [AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning](https://mlsanity.com/paper/2606.20373/) (2026)
- [WebCraftBench: Evaluating Web Application Generation from a Software Testing Perspective](https://mlsanity.com/paper/2609.15387/) (2026)

## Abstract (arXiv)

Automated web GUI testing (AWGT) approaches explore web applications through GUI actions to achieve code coverage. However, existing approaches, whether random-based, model-based, or reinforcement-learning-based, often struggle to generate continuous and semantically meaningful action sequences for testing complex functionalities, limiting their achievable code coverage. Recent LLM-based approaches partially alleviate this problem but still fall short due to limited capability in inferring testable functionalities and low success rates in executing tasks on complex web applications. In this paper, we propose ResTest, a complementary approach that uses coverage-report-guided LLMs to target residual not-covered code left by existing AWGT approaches. ResTest first runs an existing AWGT approach to broadly explore the application while constructing a state transition graph with summarized information. Once coverage plateaus, ResTest employs an LLM to infer not-covered functionalities based on the state transition graph and coverage report, and then utilizes a specialized LLM-driven GUI agent to execute these functionalities in a targeted manner. Our evaluation on ten open-source web applications shows that ResTest improves three categories of AWGT approaches by 17.52% to 24.36% in average code coverage. Ablation studies further confirm the sustained coverage improvement capability and the contribution of each component.