🤖 AI Summary
This work proposes an AI-driven autonomous testing framework that addresses the fragility of traditional web test scripts—often susceptible to UI changes and timing issues—and their inability to support natural language–guided security testing. By leveraging a containerized, decoupled architecture, the framework automatically translates natural language instructions into robust web interaction scripts while integrating OWASP Top 10 security validations. It introduces a novel semantic mapping from natural language to security probes and enhances reliability through five key strategies: context-aware selector generation, intelligent wait injection, failure-based learning, and others. Experimental results demonstrate substantial improvements: script generation success rates increase from 55% to 93%, navigation failures decrease eightfold, timing-related race conditions are reduced by 80%, and test authoring time is cut by 75%. Furthermore, security testing achieves detection rates of 85% for authentication bypasses and 95% for input validation flaws, with a false positive rate below 12%.
📝 Abstract
Modern web test suites rot. A UI refactor breaks locators, a timing change causes race conditions, and within weeks developers abandon the suite entirely. This paper presents an AI-driven autonomous testing framework that addresses these failure modes through five integrated strategies - navigation reliability, context-aware selector generation, post-generation validation, smart wait injection, and failure learning - implemented over a containerised worker architecture that decouples orchestration from long-running browser execution. Evaluated across four production applications and 176 scenarios, the framework improves script generation success from 55% to 93%, achieves an 8x reduction in navigation failures, eliminates 80% of timing-related race conditions, and reduces test creation time by 75% compared to manual Selenium authoring. The framework extends naturally to security validation: testers describe attack scenarios in plain English -"try accessing another user's invoice"- which the agent converts to OWASP Top 10-aligned browser probes, detecting 85% of authentication bypass vulnerabilities and 95% of input validation flaws with false positive rates below 12%. Natural-language-driven security testing of this kind represents, to our knowledge, a novel contribution to the field.