Enhancing Exploratory Testing by Large Language Model and Knowledge Graph

Exploratory testing leverages the tester's knowledge and creativity to design test cases for effectively uncovering system-level bugs from the end user's perspective. Researchers have worked on test scenario generation to support exploratory testing based on a system knowledge graph, enriched with scenario and oracle knowledge from bug reports. Nevertheless, the adoption of this approach is hindered by difficulties in handling bug reports of inconsistent quality and varied expression styles, along with the infeasibility of the generated test scenarios. To overcome these limitations, we utilize the superior natural language understanding (NLU) capabilities of Large Language Models (LLMs) to construct a System KG of User Tasks and Failures (SysKG-UTF). Leveraging the system and bug knowledge from the KG, along with the logical reasoning capabilities of LLMs, we generate test scenarios with high feasibility and coherence. Particularly, we design chain-of-thought (CoT) reasoning to extract human-like knowledge and logical reasoning from LLMs, simulating a developer's process of validating test scenario feasibility. Our evaluation shows that our approach significantly enhances the KG construction, particularly for bug reports with low quality. Furthermore, our approach generates test scenarios with high feasibility and coherence. The user study further proves the effectiveness of our generated test scenarios in supporting exploratory testing. Specifically, 8 participants find 36 bugs from 8 seed bugs in two hours using our test scenarios, a significant improvement over the 21 bugs found by the state-of-the-art baseline.