可重复的干扰感知移动测试

Weilun Xiong, Shihao Chen, Yuning Zhang, Mingyuan Xia, Zhengwei Qi
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引用次数: 7

摘要

移动应用生来就是要在一个不断变化的网络连接、随机硬件中断、意外任务切换等环境中工作的。然而,这种干扰情况在传统的移动测试中往往被忽略,但在实际中却频繁而复杂地发生,造成了各种鲁棒性、响应性和一致性问题。在本文中,我们提出了JazzDroid来引入干扰到移动测试。JazzDroid采用灰盒方法在二进制级别对应用程序进行检测,从而使干扰逻辑与应用程序执行内联,并且可以触发以有效地影响正常执行。然后,JazzDroid通过应用程序开发人员的现有测试反复编排仪表化应用程序,并不断随机干扰,以发现可能的错误执行。一旦发现问题,JazzDroid就会生成一个测试脚本,其中包含来自开发人员测试的用户输入和注入给开发人员的干扰,以重现问题。在高层次上,JazzDroid可以无缝地集成到应用程序开发人员的测试过程中,从现有的测试中发现更多的问题。我们实现JazzDroid功能在未经修改的应用程序直接从应用程序市场和接口事实上的工业测试工具链。JazzDroid通过发现6倍以上的问题来改进手机测试,包括崩溃、功能bug、UI一致性问题和导致大量应用失败的常见bug模式。
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Reproducible Interference-Aware Mobile Testing
Mobile apps are born to work in an environment with ever-changing network connectivity, random hardware interruption, unanticipated task switches, etc. However, such interference cases are often oblivious in traditional mobile testing but happen frequently and sophisticatedly in the field, causing various robustness, responsiveness and consistency problems. In this paper, we propose JazzDroid to introduce interference to mobile testing. JazzDroid adopts a gray-box approach to instrument apps at binary level such that interference logic is inlined with app execution and can be triggered to effectively affect normal execution. Then, JazzDroid repeatedly orchestrates the instrumented app through app developers' existing tests and continuously randomizes interference on the fly to reveal possible faulty executions. Upon discovering problems, JazzDroid generates a test script with the user inputs from developers' tests and the interference injected for developers to reproduce the problems. At a high level, JazzDroid can be seamlessly integrated into app developers' testing procedures, detecting more problems from existing tests. We implement JazzDroid to function on unmodified apps directly from app markets and interface with de facto industrial testing toolchain. JazzDroid improves mobile testing by discovering 6x more problems, including crashes, functional bugs, UI consistency issues and common bug patterns that fail numerous apps.
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