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 paper, we describe two such operators for manipulating hierarchical Statecharts: Match, for finding correspondences between models, and Merge, for combining models with respect to known 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. In this way, we automatically construct merges that preserve the semantics of Statecharts models. We illustrate and evaluate our work by applying our operators to AT&T telecommunication features. 1

Constructing comprehensive operational models of intended system behaviour is a complex and costly task. Consequently, practitioners have adopted techniques that support incremental elaboration of partial behaviour descriptions. A noteworthy example is the wide adoption of scenario-based notations such as message sequence charts. Scenario-based specifications are partial descriptions that can be incrementally elaborated to cover the system behaviour that is of interest. However, how should partial behavioural models described by different stakeholders with different viewpoints covering different aspects of behaviour be composed? How should partial models of component instances of the same type be put together? In this paper, we propose model merging as a general solution to these questions. We formally define model merging based on observational refinement and show that merging consistent models is a process that should result in a minimal common refinement. Because minimal common refinements are not guaranteed to be unique, we argue that the modeller should participate in the process of elaborating such a model. We also discuss the role of the least common refinement and the greatest lower bound of all minimal common refinements in this elaboration process. In addition, we provide algorithms for i) checking consistency between two models; ii) constructing their least common refinement if one exists; iii) supporting the construction of a minimal common refinement if there is no least common refinement.

View merging, also called view integration, is a key problem in conceptual modeling. Large models are often constructed and accessed by manipulating individual views, but it is important to be able to consolidate a set of views to gain a unified perspective, to understand interactions between views, or to perform various types of analysis. View merging is complicated by incompleteness and inconsistency: Stakeholders often have varying degrees of confidence about their statements. Their views capture different but overlapping aspects of a problem, and may have discrepancies over the terminology being used, the concepts being modeled, or how these concepts should be structured. Once views are merged, it is important to be able to trace the elements of the merged view back to their sources and to the merge assumptions related to them. In this paper, we present a framework for merging incomplete and inconsistent graph-based views. We introduce a formalism, called annotated graphs, with a built-in annotation scheme for modeling incompleteness and inconsistency. We show how structure-preserving maps can be employed to express the relationships between disparate views modeled as annotated graphs, and provide a general algorithm for merging views with arbitrary interconnections. We provide a systematic way to generate and represent the traceability information required for tracing the merged view elements back to their sources, and to the merge assumptions giving rise to the elements.

View merging, also called view integration, is a key problem in conceptual modeling. Large models are often constructed and accessed by manipulating individual views, but it is important to be able to consolidate a set of views to gain a unified perspective, to understand interactions between views, or to perform various types of end-to-end analysis. View merging is complicated by incompleteness and inconsistency of views. Once views are merged, it is useful to be able to trace the elements of the merged view back to their sources. In this paper, we propose a framework for merging incomplete and inconsistent graph-based views. We introduce a formalism, called poset-annotated graphs, which incorporates a systematic annotation scheme capable of modeling incompleteness and inconsistency as well as providing a built-in mechanism for ownership traceability. We show how structure-preserving maps can capture the relationships between disparate views modeled as poset-annotated graphs, and provide a general algorithm for merging views with arbitrary interconnections. We use the i ∗ modeling language [26] as an example to demonstrate how our approach can be applied to existing graph-based modeling languages, especially in the domain of early Requirements Engineering. 1

The use of viewpoints has long been proposed as a technique to structure evolving requirements models. In theory, viewpoints should provide better stakeholder traceability, and the ability to discover important requirements by comparing viewpoints. However, this theory has never been tested empirically. This paper reports on an exploratory case study of a key hypothesis of the viewpoints theory, namely that by creating separate viewpoint models to represent different stakeholder contributions, and explicitly merging them, important hidden requirements can be discovered. The case study compared two modelling teams using the i ∗ notation to capture requirements for new webbased counselling services for a large charitable organisation. One team used viewpoints; the other did not. The conclusions include that viewpoint merging improves the understanding of the problem domain, but is very time consuming. The process of merging was more important than the merged product. The study also indicates a need for better model management tools, as both teams encountered difficulty in managing large, evolving models. 1.

Software merging is a common and essential activity during the lifespan of large-scale software systems. Traditional textual merge techniques are inadequate for detecting syntactic merge conflicts. In this paper, we propose a domain-independent approach for syntactic software merging that exploits the graph-based structure(s) of programs. We use morphisms between fuzzy graphs to capture the relationships between the structural elements of the programs to be merged, and apply a truth ordering lattice to express inconsistencies and evolutionary properties as we compute the merge. We demonstrate the approach with a three-way consolidation merge in a commercial software system; in particular, we show how analyzing merged call structures can help developers gain a better understanding and control of software evolution. 1

Abstract. Constructing comprehensive operational models of intended system behaviour is a complex and costly task. Consequently, practitioners adopt techniques that support partial behaviour decription such as scenario-based specifications, and focus on elaborating these descriptions iteratively. In previous work, we show how this process can be formally supported by Modal Transition Systems (MTSs), observational refinement, and model merging. In this paper, we study a number of properties of merging MTSs and give insights on the implications these results have on engineering and reasoning about behaviour models. We illustrate the utility of our results on a case study. 1

Constructing comprehensive operational models of intended system behavior is a complex and costly task, which can be mitigated by the construction of partial behavior models, providing early feedback and subsequently elaborating them iteratively. However, how should partial behavior models with different viewpoints covering different aspects of behavior be composed? How should partial models of component instances of the same type be put together? In this article, we propose model merging of modal transition systems (MTSs) as a solution to these questions. MTS models are a natural extension of labelled transition systems that support explicit modeling of what is currently unknown about system behavior. We formally define model merging based on weak alphabet refinement, which guarantees property preservation, and show that merging consistent models is a process that should result in a minimal common weak alphabet refinement (MCR). In this article, we provide theoretical results and algorithms that support such a process. Finally, because in practice MTS merging is likely to be combined with other operations over MTSs such as parallel composition, we also study the algebraic properties of merging and apply these, together with the algorithms that support MTS merging, in a case study.

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