|Authors||R. Ji, Z. Li, S. Chen, M. Pan, T. Zhang, T. Yue, S. Ali and X. Li|
|Title||Uncovering Unknown System Behaviors in Uncertain Networks with Model and Search-based Testing|
|Project(s)||MBT4CPS: Model-Based Testing For Cyber-Physical Systems , Department of Engineering Complex Software Systems|
|Publication Type||Proceedings, refereed|
|Year of Publication||2018|
|Conference Name||11th IEEE Conference on Software Testing, Validation and Verification|
Modern software systems rely on information networks for communication. Such information networks are inherently unpredictable and unreliable. Consequently, software systems behave in an unstipulated manner in uncertain network conditions. Discovering unknown behaviors of these software systems in uncertain network conditions is essential to ensure their correct behaviors. Such discovery requires the development of systematic and automated methods. We propose an online and iterative Model-Based Testing approach to evolve test models with search algorithms. Our ultimate aim is to discover unknown expected behaviors that can only be observed in uncertain network conditions. Also, we implement an adaptive search-based test case generation strategy to generate test cases that are executed on the system under test. We evaluated our approach with an open source video conference application—Jitsi with four search algorithms. Results show that our approach was effective in discovering unknown system behaviors. In particular, (1+1) Evolutionary Algorithm outperformed the other three search algorithms including Random Search.