The main objective of the International Conference on Software Engineering and Formal Methods (SEFM) is to bring together practitioners and researchers from academia, industry, and government, to advance the state of the art in formal methods, to help in their large-scale application in the software industry, and to encourage their integration with other practical software engineering methods.

This poster presents a pilot project on non-conventional learning strategies based on students? active participation in real-life FLOSS projects. The aim of the project is to validate the hypothesis that the peer-production model, which underlies most FLOSS projects, can enhance the learning-teaching process based on extensive and systematic collaborative practices.

Previous work has proposed the ontology-based semi-automatic generation of antipattern Bayesian Network(BN) models. The generated BN model can be used to illustrate the effects of uncertainty on antipatterns using Bayesian propagation. This can guide users in detecting particular antipattern attributes of importance based on uncertain ontological information. However, the proposed approach has been implemented in the Protege ontology editor environment and requires human intervention to specify how the BN model will be generated. The fully automated generation of ontology-based antipattern BN models still remains an open issue. SPARSE is an OWL ontology based intelligent system that assists software project managers in the antipattern detection process. In this paper, we propose the use of the resulting detected antipatterns of SPARSE, their attributes (i.e. causes, symptoms, consequences) and the ontological relationships between these attributes, in order to automatically generate BN models of the detected antipatterns. We illustrate how this approach can be implemented using an example of 8 antipattern attributes of 6 inter-related antipatterns detected using SPARSE.

Abstract This article describes a framework to formally model and analyse human behaviour. This is shown by a simple case study of a chocolate vending machine, which represents many aspects of human behaviour. The case study is modelled and analysed using the Maude rewrite system. This work extends a previous work by Basuki which attempts to model interactions between human and machine and analyse the possibility of errors occurring in the interactions. By redesigning the interface, it can be shown that certain kinds of error can be avoided for some users. This article overcomes the limitation of Basuki’s approach by incorporating many aspects of user behaviour into a single user model, and introduces a more natural approach to model human–computer interaction.

No abstract available.

No abstract available.

Experiments with simulators allow psychologists to better understand the causes of human errors and build models of cognitive processes to be used in human reliability assessment (HRA). This paper investigates an approach to task failure analysis based on patterns of behaviour, by contrast to more traditional event-based approaches. It considers, as a case study, a formal model of an air traffic control (ATC) system which incorporates controller behaviour. The cognitive model is formalised in the CSP process algebra. Patterns of behaviour are expressed as temporal logic properties. Then a model-checking technique is used to verify whether the decomposition of the operator's behaviour into patterns is sound and complete with respect to the cognitive model. The decomposition is shown to be incomplete and a new behavioural pattern is identified, which appears to have been overlooked in the analysis of the data provided by the experiments with the simulator. This illustrates how formal analysis of operator models can yield fresh insights into how failures may arise in interactive systems.