Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2602.24108v1</id>\n    <title>Context-Aware Functional Test Generation via Business Logic Extraction and Adaptation</title>\n    <updated>2026-02-27T15:47:37Z</updated>\n    <link href='https://arxiv.org/abs/2602.24108v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2602.24108v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>Functional testing is essential for verifying that the business logic of mobile applications aligns with user requirements, serving as the primary methodology for quality assurance in software development. Despite its importance, functional testing remains heavily dependent on manual effort due to two core challenges. First, acquiring and reusing complex business logic from unstructured requirements remains difficult, which hinders the understanding of specific functionalities. Second, a significant semantic gap exists when adapting business logic to the diverse GUI environments, which hinders the generation of test cases for specific mobile applications. To address the preceding challenges, we propose LogiDroid, a two-stage approach that generates individual functional test cases by extracting business logic and adapting it to target applications. First, in the Knowledge Retrieval and Fusion stage, we construct a dataset to retrieve relevant cases and extract business logic for the target functionality. Second, in the Context-Aware Test Generation stage, LogiDroid jointly analyzes the extracted business logic and the real-time GUI environment to generate functional test cases. This design allows LogiDroid to accurately understand application semantics and use domain expertise to generate complete test cases with verification assertions. We assess the effectiveness of LogiDroid using two widely-used datasets that cover 28 real-world applications and 190 functional requirements. Experimental results show that LogiDroid successfully tested 40% of functional requirements on the FrUITeR dataset (an improvement of over 48% compared to the state-of-the-art approaches) and 65% on the Lin dataset (an improvement of over 55% compared to the state-of-the-art approaches). These results demonstrate the significant effectiveness of LogiDroid in functional test generation.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.SE'/>\n    <published>2026-02-27T15:47:37Z</published>\n    <arxiv:primary_category term='cs.SE'/>\n    <author>\n      <name>Yakun Zhang</name>\n    </author>\n    <author>\n      <name>Zihan Wang</name>\n    </author>\n    <author>\n      <name>Xinzhi Peng</name>\n    </author>\n    <author>\n      <name>Zihao Xie</name>\n    </author>\n    <author>\n      <name>Xiaodong Wang</name>\n    </author>\n    <author>\n      <name>Xutao Li</name>\n    </author>\n    <author>\n      <name>Dan Hao</name>\n    </author>\n    <author>\n      <name>Lu Zhang</name>\n    </author>\n    <author>\n      <name>Yunming Ye</name>\n    </author>\n  </entry>"
}