Abstract—This paper reports on work that is investigating

Abstract—Model Management addresses the problem of managing an evolving collection of models, by capturing the relationships between models and providing well-defined operators to manipulate them. In this article, we describe two such operators for manipulating feature specifications described using hierarchical state machine models: Match, for finding correspondences between models, and Merge, for combining models with respect to known or hypothesized correspondences between them. Our Match operator is heuristic, making use of both static and behavioural properties of the models to improve the accuracy of matching. Our Merge operator preserves the hierarchical structure of the input models, and handles differences in behaviour through parameterization. This enables us to automatically construct merges that preserve the semantics of hierarchical state machines. We report on tool support for our Match and Merge operators, and illustrate and evaluate our work by applying these operators to a set of telecommunication features built by AT&T.

Abstract. Model composition helps designers managing complexities by modeling different system views separately, and later compose them into an integrated model. In the past years, researchers have focused on the definition of model composition approaches (operators) and the tools supporting them (model composition engines). Testing model composition engines is hard. It requires the synthesis and analysis of complex data structures (models). In this context, synthesis means to assembly complex structures in a coherent way with respect to semantic constraints. In this paper we propose to automatically synthesize input data for model composition engines using a model decomposition operator. Through this operator we synthesize models in a coherent way, satisfying semantic constraints and taking into account the complex mechanics involved in the model composition. Furthermore, such operator enables a straightforward analysis of the composition result.

Software development today takes place in the context of a complex system-of-systems that includes a broad technological infrastructure along with a wide set of human activities. The technological systems and the human activity systems have a symbiotic relationship- each shapes the other in complex ways, such that neither can be understood in isolation. A recent report from the SEI on Ultra-Large Scale (ULS) Systems accurately characterized the nature of these systems-of- systems: they have no centralized control; experience normal failures and continual evolution of heterogeneous elements; and their requirements are inherently conflicting, diverse and often unknowable. For design purposes, the boundary between people and software disappears- design is as much about shaping the human activities as it is about constructing the software. Although the SEI report focussed on the extreme scale, it is clear that most of

Merging and integrating different requirement specification models which have been developed by domain experts and analysts with dissimilar perspectives on the same issue has been the subject of tremendous amount of research. In this research proposal, we intend to focus on the fact that human analysts ’ opinions possess a degree of uncertainty which can be exploited while integrating conceptual models. We propose an underlying modeling construct which is the basis for transforming conceptual models into a manipulatable format. Based on this construct, we propose to develop methods for negotiating over and merging of conceptual models on top of an extension to the Dempster-Shafer theory of evidence called Subjective logic. The approach shall mainly focus on the formalization of uncertainty and expert reliability through the employment of belief theory. We are also interested in creating a model for pre-consensus negotiation among the involved viewpoints in the conceptual modeling process.

Software’s ability to adapt at run-time to changing user needs, system intrusions or faults, changing operational environment, and resource variability has been proposed as a means to cope with the complexity of today’s softwareintensive systems. Such self-adaptive systems can configure and reconfigure themselves, augment their functionality, continually optimize themselves, protect themselves, and recover themselves, while keeping most of their complexity hidden from the user and administrator. In this paper, we present research road map for software engineering of selfadaptive systems focusing on four views, which we identify as essential: requirements, modelling, engineering, and assurances.

Very large systems have an architecture that is designed to allow them to evolve through a long life. Such systems are developed by teams of architects. One of the first things the architects do is make a model of their architecture. This model constitutes the formal architecture description based on which software engineers will eventually build the real system. The architecture model is normally governed by a specialised metamodel whose rules determine the consistency and completeness of the description. The development of a system architecture is carried out cooperatively but independently by team members. Consequently it is quite normal for the architecture description as a whole to be both incomplete and inconsistent. The architects strive to eventually produce a complete overall (i.e. merged) description and to eliminate the inconsistencies. By means of an example, we show how and why the architecture model and the metamodel must co-evolve. We describe a design tool that we have developed to support this process of co-evolution. The tool allows a team of architects to detect inconsistencies in their separate and merged models. The tool tolerates inconsistencies. It produces reports of inconsistencies which then become targets for removal as the whole architecture description evolves. 1.

der Software-Entwicklung nicht mehr ausreichen. Die hohen Qualitätsanforderungen einerseits, die starken zeitlichen Randbedingungen an die Entwicklung andererseits können nur mit dem Einsatz einer modellbasierten Entwicklung erfüllt werden. Da diese Modelle in der Regel ausführbar sind, können bereits in frühen Entwicklungsphasen Simulationen durchgeführt und die Funktionen durch Versuche im Fahrzeug erprobt und validiert werden.

A key problem in model-based development is integrating a collection of models into a single, larger, specification as a way to construct a functional system, to develop a unified understanding, or to enable automated reasoning about properties of the resulting system. In this article, we suggest that the choice of a particular model integration operator depends on the inter-model relationships that hold between individual models. Based on this observation, we distinguish three key integration operators studied in the literature – merge, composition and weaving – and describe each operator along with the notion of relationship that underlies it. We then focus on the merge activity and provide a detailed look at the factors that one must consider in defining a merge operator, particularly the way in which the relationships should be captured during merge. We illustrate these factors using two merge operators that we have developed in our earlier work for combining models that originate from distributed teams.

Abstract. When building large-scale goal-oriented models using the i* framework, the problem of scalability arises. One of the most important causes for this problem is the lack of modularity constructs in the language: just the concept of actor boundary allows grouping related model elements. In this paper, we present an approach that incorporates modules into the i * framework with the purpose of ameliorating the scalability problem. We explore the different types of modules that may be conceived in the framework, define them in terms of an i * metamodel, and introduce different model operators that support their application.