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<p>Data manipulation functionalities (DMFs) refer to operations such as create, read, update and delete which are crucial for maintaining the integrity of Android application (app) data. Existing testing techniques often fail to explicitly verify DMFs in combination, having limited capability to uncover non-crashing data manipulation errors (DMEs). In this paper, we propose a novel approach <Emphasis FontCategory="SansSerif">SceneData</Emphasis>, which utilizes a scene-guided exploration strategy to effectively detect DMEs in Android apps. Particularly, we model the UI pages as scenes and present a scene transition graph (SceneTG) to capture the relation between scenes. By utilizing SceneTG, we explore app states to thoroughly validate DMFs across different scenes. We evaluate <Emphasis FontCategory="SansSerif">SceneData</Emphasis> on 17 popular real-world apps, and <Emphasis FontCategory="SansSerif">SceneData</Emphasis> discovers 25 previously unknown bugs in their latest releases, 21 of which are non-crashing DMEs. The experiments also show that a significant fraction (15/21) of these bugs cannot be detected by the state-of-the-art techniques.</p>

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Detecting data manipulation errors in android applications using scene-guided exploration

  • 期刊论文

  • 发表日期：

    2025年5月10日

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  • Shuqi Liu,
  • Yu Zhou,
  • Wenhua Yang,
  • Taolue Chen,
  • Harald Gall

摘要

Data manipulation functionalities (DMFs) refer to operations such as create, read, update and delete which are crucial for maintaining the integrity of Android application (app) data. Existing testing techniques often fail to explicitly verify DMFs in combination, having limited capability to uncover non-crashing data manipulation errors (DMEs). In this paper, we propose a novel approach SceneData, which utilizes a scene-guided exploration strategy to effectively detect DMEs in Android apps. Particularly, we model the UI pages as scenes and present a scene transition graph (SceneTG) to capture the relation between scenes. By utilizing SceneTG, we explore app states to thoroughly validate DMFs across different scenes. We evaluate SceneData on 17 popular real-world apps, and SceneData discovers 25 previously unknown bugs in their latest releases, 21 of which are non-crashing DMEs. The experiments also show that a significant fraction (15/21) of these bugs cannot be detected by the state-of-the-art techniques.