Test Agents: Adaptive, Autonomous and Intelligent Test Cases

Growth of so‰ware size, lack of resources to perform regression testing, and failure to detect bugs faster have seen increased reliance on continuous integration and test automation. Even with greater hardware and so‰ware resources dedicated to test automation, so‰ware testing is faced with enormous challenges, resulting in increased dependence on complex mechanisms for automated test case selection and prioritization as part of a continuous integration framework. Œese mechanisms are currently using simple entities called test cases that are concretely realized as executable scripts. Our key idea is to provide test cases with more reasoning, adaptive behavior and learning capabilities by using the concepts of intelligent so‰ware agents. We refer to such test cases as test agents. Œe model that underlie a test agent is capable of ƒexible and autonomous actions in order to meet overall testing objectives. Our goal is to increase the decentralization of regression testing by leŠing test agents to know for themselves when they should be executing, how they should update their purpose, and when they should interact with each other. In this paper, we envision so‰ware test agents that display such adaptive autonomous behavior. Emerging developments and challenges regarding the use of test agents are explored—in particular, new research that seeks to use adaptive autonomous agents in so‰ware testing. ACM Reference format: Eduard Paul Enoiu and Mirgita Frasheri. 2016. Test Agents: Adaptive, Autonomous and Intelligent Test Cases. In Proceedings of ACM Conference, Washington, DC, USA, July 2017 (Conference’17), 4 pages. DOI: 10.1145/nnnnnnn.nnnnnnn