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Cunming Zhang

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Preprint Aug 2026

Assessing Behavioral Validation in UI Component Test Suites Using Inferred Metamorphic Relations

UI component libraries are commonly assessed using execution-based metrics such as statement and branch coverage, yet these metrics provide limited insight into whether tests verify the behavioral relations implied by component APIs and documentation. This paper presents an MR-based framework that uses inferred metamorphic relations (MRs) as an empirical behavioral reference, rather than a complete specification, for assessing UI component test suites. Given a component's source, documentation, and tests, the framework infers component-specific MRs using a UI-specific taxonomy, aligns tests with the inferred relations through hybrid deterministic and semantic analysis, and computes relation-level MR coverage metrics. We manually validate both the inferred MR space and the test--MR alignment. Our evaluation shows that existing test suites exercise substantially more behavioral relations than they explicitly validate: MR Cover remains between 42.5% and 47.6% across three LLM configurations and consistently below MR Touch. Most uncovered relations are weak-oracle cases, where behaviors are exercised but lack explicit behavioral validation. MR coverage also complements execution-based coverage by revealing behavioral gaps not reflected by statement or branch coverage alone. We further assess practical relevance through issue-description mapping, oracle strengthening, and MR-relevant injected faults. Most reported issue descriptions can be mapped to inferred MR relation types; weak-oracle relations often expose missing validation evidence; and MR labels show a trend in MR-relevant fault detection. Overall, MR coverage provides a complementary relation-level perspective for assessing behavioral validation in modern UI component testing.

Yu Pei, Cunming Zhang, Jeongju Sohn et al. · 0 citations
Preprint Aug 2026

From Bug Reports to Browser-Executable Procedures: An LLM-Driven Agent for Web GUI Bug Reproduction

Reproducing web GUI bugs from natural-language bug reports is critical for software maintenance, but remains difficult because reports often lack prerequisites such as dependencies and input files. Existing bug reproduction techniques mainly target code units or mobile applications and lack end-to-end visual execution and validation for web GUIs. We present ReBug, a context-aware agent system that reconstructs, executes, and validates browser-level reproduction procedures from web GUI bug reports by driving a real browser. ReBug separates reproduction into two stages. In the preparation stage, ReBug reconstructs missing prerequisites from the report and available artifacts, and it produces a high-level reproduction plan. In the execution stage, it performs tool-mediated interactions in the browser, maintains structured summaries of page state and action history, and validates the final state against expectations derived from the report. We evaluate ReBug on 667 real-world bug reports from four open-source web applications. On controlled current deployments, ReBug outperforms both baselines, achieving an average RSR of 49.96%, a mean task completion rate of 74.96%, and a mean action execution success rate of 86.54%. Our results show that explicit context reconstruction and state-aware browser execution effectively support report-derived browser reproduction, while historical replay shows that successful procedures often expose the original bug-present behavior on restored buggy versions.

Cunming Zhang, Yu Pei, Michail Papadakis · 0 citations

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