In this paper we present an experience report for the RMQFMU, a plug and play tool, that enables feeding data to/from an FMI2-based co-simulation environment based on the AMQP protocol. Bridging the co-simulation to an external environment allows on
one side to feed historical data to the co-simulation, serving different purposes, such as visualisation and/or data analysis. On the other side, such a tool facilitates the realisation of the digital twin concept by coupling co-simulation and hardware/robots close to real-time. In the paper we present limitations of the initial version of the RMQFMU with respect to the capability of bridging co-simulation with the real world. To provide the desired functionality of the tool, we present in a step-by-step fashion how these limitations, and subsequent limitations, are alleviated. We perform various experiments in order to give reason to the modifications carried out. Finally, we report on two case-studies where we have adopted the RMQFMU, and provide guidelines meant to aid practitioners in its use.
Test automation is common in software development; often one tests repeatedly to identify regressions. If the amount of test cases is large, one may select a subset and only use the most important test cases. The regression test selection (RTS) could
be automated and enhanced with Artificial Intelligence (AI-RTS). This however could introduce ethical challenges. While such challenges in AI are in general well studied, there is a gap with respect to ethical AI-RTS. By exploring the literature and learning from our experiences of developing an industry AI-RTS tool, we contribute to the literature by identifying three challenges (assigning responsibility, bias in decision-making and lack of participation) and three approaches (explicability, supervision and diversity). Additionally, we provide a checklist for ethical AI-RTS to help guide the decision-making of the stakeholders involved in the process.
The emergence of new technologies in software testing has increased the automation and flexibility of the testing process. In this context, the adoption of agents in software testing remains an active research area in which various agent methodologie
s, architectures, and tools are employed to improve different test problems. Even though research that investigates agents in software testing has been growing, these agent-based techniques should be considered in a broader perspective. In order to provide a comprehensive overview of this research area, which we define as agent-based software testing (ABST), a systematic mapping study has been conducted. This mapping study aims to identify the topics studied within ABST, as well as examine the adopted research methodologies, identify the gaps in the current research and point to directions for future ABST research. Our results suggest that there is an interest in ABST after 1999 that resulted in the development of solutions using reactive, BDI, deliberative and cooperate agent architectures for software testing. In addition, most of the ABST approaches are designed using the JADE framework, have targeted the Java programming language, and are used at system-level testing for functional, non-functional and white-box testing. In regards to regression testing, our results indicate a research gap that could be addressed in future studies.
Growth of software 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 software resources dedicated to tes
t automation, software 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. These 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 software agents. We refer to such test cases as test agents. The model that underlie a test agent is capable of flexible and autonomous actions in order to meet overall testing objectives. Our goal is to increase the decentralization of regression testing by letting 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 software 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 software testing.
