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This paper presents a novel approach for automated analysis of process models discovered using process mining techniques. Process mining explores underlying processes hidden in the event data generated by various devices. Our proposed Inductive machine learning method was used to build business process models based on actual event log data obtained from a hotel's Property Management System (PMS). The PMS can be considered as a Multi Agent System (MAS) because it is integrated with a variety of external systems and IoT devices. Collected event log combines data on guests stay recorded by hotel staff, as well as data streams captured from telephone exchange and other external IoT devices. Next, we performed automated analysis of the discovered process models using formal methods. Spin model checker was used to simulate process model executions and automatically verify the process model. We proposed an algorithm for the automatic transformation of the discovered process model into a verification model. Additionally, we developed a generator of positive and negative examples. In the verification stage, we have also used Linear temporal logic (LTL) to define requested system specifications. We find that the analysis results will be well suited for process model repair.

In this paper, we research the use of software combinatorial testing techniques and the Formal Concept Analysis method for preparing sets of questions for student assessment in e-learning systems. Utilizing these techniques and methods, we ensure that the selected questions optimally cover the course material and that each question combines multiple topics. Therefore, in this paper we introduce our method for preparing student assessments that performs automated combinatorial testing and selection of questions, as well as automated generation of appropriate sequences of questions. The input for our method is a set of questions labelled with attributes or features. This set of questions is pre-processed using the Formal Concept Analysis method, and then the combinatorial testing of question features is performed, which generates a concise list of test-cases covering all pairs or triples of question features. Correspondingly, our method helps in identifying and selecting a subset of questions that covers all generated test-cases. Afterwards, the Formal Concept Analysis method automatically generates suitable sequences of selected questions for formative student assessments in e-learning systems. In this paper we implemented the proposed combinatorial testing method, and also demonstrated the feasibility of the proposed method on a use-case from an actual e-learning system.