<p>Fragile web tests, primarily caused by locator breakages, are a persistent challenge in web development. Hence, researchers have proposed techniques for web-element re-identification in which algorithms utilize a range of element properties to relocate elements on updated versions of websites based on similarity scoring. In this paper, we replicate the original studies of the most recent propositions in the literature, namely the Similo algorithm and its successor, VON Similo. We also acknowledge and reconsider assumptions related to threats to validity in the original studies, which prompted additional analysis and the development of mitigation techniques. Our analysis revealed that VON Similo, despite its novel approach, tends to produce more false positives than Similo. We mitigated these issues through algorithmic refinements and optimization algorithms that enhance parameters and comparison methods across all Similo variants, improving the accuracy of Similo on its original benchmark by 5.62%. Moreover, we extend the replicated studies by proposing a larger evaluation benchmark (23<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\times \)</EquationSource> </InlineEquation> bigger than the original study) as well as a novel approach that combines the strengths of both Similo and VON Similo, called HybridSimilo. The combined approach achieved a gain comparable to the improved Similo alone. Results on the extended benchmark show that HybridSimilo locates 98.8% of elements with broken locators in our testing scenarios.</p>

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Web element relocalization in evolving web applications: A comparative analysis and extension study

  • Anton Kluge,
  • Andrea Stocco

摘要

Fragile web tests, primarily caused by locator breakages, are a persistent challenge in web development. Hence, researchers have proposed techniques for web-element re-identification in which algorithms utilize a range of element properties to relocate elements on updated versions of websites based on similarity scoring. In this paper, we replicate the original studies of the most recent propositions in the literature, namely the Similo algorithm and its successor, VON Similo. We also acknowledge and reconsider assumptions related to threats to validity in the original studies, which prompted additional analysis and the development of mitigation techniques. Our analysis revealed that VON Similo, despite its novel approach, tends to produce more false positives than Similo. We mitigated these issues through algorithmic refinements and optimization algorithms that enhance parameters and comparison methods across all Similo variants, improving the accuracy of Similo on its original benchmark by 5.62%. Moreover, we extend the replicated studies by proposing a larger evaluation benchmark (23 \(\times \) bigger than the original study) as well as a novel approach that combines the strengths of both Similo and VON Similo, called HybridSimilo. The combined approach achieved a gain comparable to the improved Similo alone. Results on the extended benchmark show that HybridSimilo locates 98.8% of elements with broken locators in our testing scenarios.