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The paper introduces WebTestPilot, an LLM-based agent for end-to-end web testing against natural language specifications that addresses the challenges of implicit oracle inference and probabilistic reasoning. WebTestPilot uses a symbolization layer to represent GUI elements as symbols and translates natural language into step-by-step instructions with inferred pre- and post-conditions over these symbols, effectively capturing data, temporal, and causal dependencies for validation. Experiments on a new benchmark of bug-injected web applications demonstrate that WebTestPilot achieves a 99% task completion rate with 96% precision and 96% recall in bug detection, significantly outperforming existing LLM-based approaches.
LLM agents can now achieve near-perfect accuracy in end-to-end web testing by symbolizing GUI elements and inferring pre/post-condition oracles, blowing away previous approaches.
Visual language model (VLM) agents show great promise in automating end-to-end (E2E) web testing against requirements in natural language. However, the probabilistic nature of language models can have inherent hallucinations. Therefore, given a detected inconsistency between the requirement and the web application, it is hard to distinguish whether it stems from the hallucination or a real application bug. Addressing this issue presents two core technical challenges: the implicit oracle inference challenge, where the agent must act as its own oracle to implicitly decide if the application's behavior is correct without guidance, and the probabilistic inference challenge, where an LLM's inconsistent reasoning undermines its trustworthiness as an oracle. Existing LLM-based approaches fail to capture such implicit oracles, either by treating any page navigation that doesn't crash as a success, or by checking each state in isolation, thus missing bugs dependent on context from prior steps. We introduce WebTestPilot, an LLM-based agent designed to address these challenges. WebTestPilot uses (1) a symbolization layer which detects and symbolizes critical GUI elements on the web application into symbols (i.e., variables) and (2) translates natural language specification into a sequence of steps, each of which is equipped with inferred pre- and post-conditions over the symbols as an oracle. This oracle captures data, temporal, and causal dependencies, enabling the validation of implicit requirements. To advance research in this area, we build a benchmark of bug-injected web apps for evaluating NL-to-E2E testing. The results show that WebTestPilot achieves a task completion rate of 99%, with 96% precision and 96% recall in bug detection, outperforming the best baseline (+70 precision, +27 recall). The agent generalizes across diverse natural language inputs and model scales.
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