基於狀態機覆蓋率的黑箱測試案例生成－以行動裝置應用程式為例

Abstract

智慧型裝置應用程式目前在軟體工業上有許多的機會以及新的挑戰，大部分的應用程式是由小團隊所開發出來，他們可能無法支付太多額外的人力成本去維護。本研究使用軟體工具去挖掘應用程式的行為，抓取一個待測物的行為及畫面資訊以建成有限狀態機。而主要研究部分探討以涵蓋率為基礎的測試用例生成技術與錯誤揭示能力的關係，另外，也提出了一個新的測試用例生成演算法，結合了能以較短的時間覆蓋較多的狀態，比起以往的演算法，也能更有效地揭示出待測物的錯誤。

Mobile applications (apps) present both great opportunities and new challenges to software industry. Most of the apps are developed by small teams that could not afford expensive software process and maintain appropriate documentations. It would be interesting and pragmatic to develop testing tools to help test engineers in such a situation. We develop a software tool that crawls through the screen activities of an app to automatically learn the finite-state machine (FSM) of the app and then generates test cases with a certain adequacy criterion. A research issue is how traditional coverage techniques work with such learned FSMs. We evaluate the bug-revealing capabilities of previous test case generation algorithms for such FSMs. Then we propose a new test case generation algorithm and carried out experiment to compare our algorithm with previous algorithms in the literature. We found that our algorithm is more efficient and more effective in revealing bugs than the others.