Hridya Dhulipala, Xiaokai Rong, Aashish Yadavally, Tien N. Nguyen · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.25601 · cited by 0 works (OpenAlex) · open-access PDF
Die automatisierte Generierung effizienter End-to-End-Webtests bleibt teuer: Entwickler müssen stabile Selektoren identifizieren, mehrstufige Workflows kodieren, Aussagen anfügen und Skripte wie Schnittstellen weiterentwickeln. Neuere LLM-basierte Webagenten können in einer Live-Browser-Sitzung natürlichsprachige Aufgaben ausführen, aber die Vollendung von Aufgaben ist nicht dasselbe wie die Herstellung eines Testware-Artifaktes - Code, der ausführbar, wiederholbar aus einem Reset-Status, von Entwicklern interaktiver und an ein Oracle gebunden ist. Wir präsentieren TESSARA, ein adaptives Test-Script-Generations-Framework, das natürliche-Sprach-Test-Absichten in Playwright-
Machine translation (Meta M2M100); the English original follows.
Automated generation of end-to-end web tests remains costly: developers must identify stable selectors, encode multi-step workflows, attach assertions, and maintain scripts as interfaces evolve. Recent LLM-based web agents can complete natural-language tasks in a live browser session, but task completion is not the same as producing a test-ware artifact - code that is executable, replay-able from a reset state, inspectable by developers, and tied to an oracle. We present TESSARA, an adaptive test-script generation framework that converts natural-language test intent into Playwright scripts consisting of executable interaction code, goal-state assertions, and step-level provenance linking each step to its generated code fragment, selector evidence, execution outcome, and repair history. TESSARA grounds each planned step via Step-Aligned Scripting (SAS), enabling fine-grained failure localization, selective regeneration of failing suffixes, and reversibility-aware backtracking that avoids undoing likely commit points (e.g., form submissions). A three-level verification pipeline (syntactic, execution, and semantic/oracle), detects and repairs malformed code, interaction failures, and incorrect landing states. We evaluate TESSARA on three public web benchmarks, measuring script executability, oracle satisfaction, replay stability, and repair efficiency, and show substantial improvements over baselines and underlying VLMs used directly as script generators.
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Topics: Computer science, Scripting language, Commit, Executable, Web application, Programming language
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