Foundations for Behavioural Model Elaboration Using Modal Transition Systems

Modal Transition Systems (MTS) are an extension of Labelled Transition Systems (LTS) that have been shown to be useful to reason about system behaviour in the context of partial information. MTSs distinguish between required, proscribed and unknown behaviour and come equipped with a notion of refinement that supports incremental modelling where unknown behaviour is iteratively elaborated into required or proscribed behaviour. A particularly useful notion in the context of software and requirements engineering is that of “merge”. Merging two consistent models is a process that should result in a minimal common refinement of both models where consistency is defined as the existence of one common refinement. One of the current limitations of MTS merging is that a complete and correct algorithm for merging has not been developed. Hence, an engineer attempting to merge partial descriptions may be prevented to do so by overconstrained algorithms or algorithms that introduce behaviour that does not follow from the partial descriptions being merged. In this thesis we study the problems of consistency and merge for the existing MTSs semantics - strong and weak semantics - and provide a complete characterization of MTS consistency as well as a complete and correct algorithm for MTS merging using these semantics. Strong and weak semantics require MTS models to have the same communicating alphabet, the latter allowing the use of a distinguished unobservable action. In this work we show that the requirement of fixing the alphabet for MTS semantics and the treatment of observable actions are limiting if MTSs are to support incremental elaboration of partial behaviour models. We present a novel observational semantics for MTS, branching alphabet semantics, inspired by branching LTS equivalence, which supports the elaboration of model behaviour including the extension of the alphabet of the system to describe behaviour aspects that previously had not been taken into account. Furthermore, we show that some unintuitive refinements allowed by weak semantics are avoided, and prove a number of theorems that relate branching refinement with alphabet refinement and consistency. These theorems, which do not hold for other semantics, support the argument for considering branching alphabet as a sound semantics to support behaviour model elaboration

Partial Behavioural Models for Requirements and Early Design

The talk will discuss the problem of creation, management, and specifically merging of partial behavioural models, expressed as model transition systems. We argue why this formalism is essential in the early stages of the software cycle and then discuss why and how to merge information coming from different sources using this formalism. The talk is based on papers presented in FSE\u2704 and FME\u2706 and will also include emerging results on synthesizing partial behavioural models from temporal properties and scenarios

On Consistency and Merge of Modal Transition Systems ABSTRACT

In this paper we provide a complete characterization of MTS consistency and propose an algorithm for MTS merging that improves on state of the art. 1

Foundations for behavioural model elaboration using modal transition systems

Modal Transition Systems (MTS) are an extension of Labelled Transition Systems (LTS) that have been shown to be useful to reason about system behaviour in the context of partial information. MTSs distinguish between required, proscribed and unknown behaviour and come equipped with a notion of refinement that supports incremental modelling where unknown behaviour is iteratively elaborated into required or proscribed behaviour. A particularly useful notion in the context of software and requirements engineering is that of “merge”. Merging two consistent models is a process that should result in a minimal common refinement of both models where consistency is defined as the existence of one common refinement. One of the current limitations of MTS merging is that a complete and correct algorithm for merging has not been developed. Hence, an engineer attempting to merge partial descriptions may be prevented to do so by overconstrained algorithms or algorithms that introduce behaviour that does not follow from the partial descriptions being merged. In this thesis we study the problems of consistency and merge for the existing MTSs semantics - strong and weak semantics - and provide a complete characterization of MTS consistency as well as a complete and correct algorithm for MTS merging using these semantics. Strong and weak semantics require MTS models to have the same communicating alphabet, the latter allowing the use of a distinguished unobservable action. In this work we show that the requirement of fixing the alphabet for MTS semantics and the treatment of observable actions are limiting if MTSs are to support incremental elaboration of partial behaviour models. We present a novel observational semantics for MTS, branching alphabet semantics, inspired by branching LTS equivalence, which supports the elaboration of model behaviour including the extension of the alphabet of the system to describe behaviour aspects that previously had not been taken into account. Furthermore, we show that some unintuitive refinements allowed by weak semantics are avoided, and prove a number of theorems that relate branching refinement with alphabet refinement and consistency. These theorems, which do not hold for other semantics, support the argument for considering branching alphabet as a sound semantics to support behaviour model elaboration.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Supporting incremental behaviour model elaboration

Behaviour model construction remains a difficult and labour intensive task which hinders the adoption of model-based methods by practitioners. We believe one reason for this is the mismatch between traditional approaches and current software development process best practices which include iterative development, adoption of use-case and scenario-based techniques and viewpoint- or stakeholder-based analysis; practices which require modelling and analysis in the presence of partial information about system behaviour. Our objective is to address the limitations of behaviour modelling and analysis by shifting the focus from traditional behaviour models and verification techniques that require full behaviour information to partial behaviour models and analysis techniques, that drive model elaboration rather than asserting adequacy. We aim to develop sound theory, techniques and tools that facilitate the construction of partial behaviour models through model synthesis, enable partial behaviour model analysis and provide feedback that prompts incremental elaboration of partial models. In this paper we present how the different research threads that we have and currently are developing help pursue this vision as part of the “Partial Behaviour Modelling—Foundations for Iterative Model Based Software Engineering” Starting Grant funded by the ERC. We cover partial behaviour modelling theory and construction, controller synthesis, automated diagnosis and refinement, and behaviour validation.Fil: Uchitel, Sebastian. Imperial College London; Reino Unido. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Alrajeh, Dalal. Imperial College London; Reino UnidoFil: Ben David, Shoham. University of Toronto; CanadáFil: Braberman, Victor Adrian. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Chechik, Marsha. University of Toronto; CanadáFil: de Caso, Guido. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: D'ippolito, Nicolás Roque. Imperial College London; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fischbein, Dario. Imperial College London; Reino UnidoFil: Garbervetsky, Diego David. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kramer, Jeff. Imperial College London; Reino UnidoFil: Russo, Alessandra. Imperial College London; Reino UnidoFil: Sibay, German. Imperial College London; Reino Unid