PADRAIG：精确安卓自动化输入生成
PADRAIG: Precise Android Automated Input Generation
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中文总结 AI 辅助
PADRAIG是基于模型的安卓测试输入生成框架，通过AUT控制流模型提升行覆盖率，在19款安卓应用上，其覆盖率比现有技术高16%，耗时减少84%。
中文摘要 AI 辅助
安卓自动化测试输入生成十余年来一直是热门研究课题，各类方法已展现出良好效果。随机输入生成是常用且易维护的方式，但效率低下；系统化及基于搜索的方法能生成有效测试，但生成耗时过长；基于模型的方法需对被测应用（AUT）建模，存在额外开销，却可加快测试生成速度。本文提出Precise AnDRoid Automated Input Generation（PADRAIG），这是一种基于模型的测试输入生成框架，利用AUT的详细控制流模型生成测试，能实现更高的行覆盖率，且测试生成耗时低于现有技术。我们从F-Droid应用商店随机选取19款安卓应用，将PADRAIG与3种采用不同测试输入生成技术的现有工具，对比行覆盖率及生成耗时。结果显示，PADRAIG平均比现有技术多实现16%的AUT覆盖率，生成测试的平均耗时减少84%。
英文摘要
Android automated test input generation has been a highly researched topic for over a decade and has shown promising results with a variety of approaches. Random input generation is commonly used and the easiest to maintain, but ultimately inefficient. Systematic and search-based approaches produce effective tests but require a disproportionally large generation runtime. Model-based approaches have the additional overhead of modelling the application under test (AUT) but they result in a faster test generation. In this paper we present Precise AnDRoid Automated Input Generation (PADRAIG), a model-based test input generation framework that uses a detailed control flow model of the AUT to generate tests that can achieve higher line coverage, with a lower test generation runtime than the state of the art. We compare the line coverage achieved, and the generation runtime of PADRAIG against 3 state of the art tools, each of which uses a different test input generation technique. Our results, using 19 randomly selected Android apps from the F-Droid application store, show that PADRAIG achieves, on average, 16% more coverage of the AUT than the state of the art and it can generate tests with, on average, 84% less runtime.
