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The use of Bayesian networks has been shown to be powerful for supporting decision making, for example in a medical context. A particularly useful inference task is the most probable explanation (MPE), which provides the most likely assignment to all the random variables that is consistent with the given evidence. A downside of this MPE solution is that it is static and not very informative for (medical) domain experts. In our research to overcome this problem, we were inspired by recent research results on augmenting Bayesian networks with argumentation theory. We use arguments to generate explanations of the MPE solution in natural language to make it more understandable for the domain expert. Moreover, the approach allows decision makers to further explore explanations of different scenarios providing more insight why certain alternative explanations are considered less probable than the MPE solution.

Tools that perform automated software testing via the user interface rely on an action selection mechanism that at each step of the testing process decides what to do next. This mechanism is often based on random choice, a practice commonly referred to as monkey testing. In this work we evaluate a first approach to genetic programming (GP) for action selection that involves evolving IF-THEN-ELSE rules; we carry out experiments and compare the results with those obtained by random selection and also by \(\mathcal {Q}\)-learning, a reinforcement learning technique. Three applications are used as Software Under Test (SUT) in the experiments, two of which are proprietary desktop applications and the other one an open source web-based application. Statistical analysis is used to compare the three action selection techniques on the three SUTs; for this, a number of metrics are used that are valid even under the assumption that access to the source code is not available and testing is only possible via the GUI. Even at this preliminary stage, the analysis shows the potential of GP to evolve action selection mechanisms.

Energy is becoming a key resource for IT systems. Hence, it can be essential for the success of a system under development to be able to derive and optimise its resource consumption. For large IT systems, compositionality is a key property in order to be applicable in practice. If such a method is hardware parametric, the effect of using different algorithms or running the same software on different hardware configurations can be studied. This article presents a hardware-parametric, compositional and precise type system to derive energy consumption functions. These energy functions describe the energy consumption behaviour of hardware controlled by the software. This type system has the potential to predict energy consumptions of algorithms and hardware configurations, which can be used on design level or for optimisation.