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.

Requirements elicitation involves the construction of large sets of conceptual models. An important step in the analysis of these models is checking their consistency. Existing research largely focuses on checking consistency of individual models and of relationships between pairs of models. However, such strategy does not guarantee global consistency. In this paper, we propose a consistency checking approach that addresses this problem for homogeneous models. Given a set of models and a set of relationships between them, our approach works by first constructing a merged model and then verifying this model against the consistency constraints of interest. By keeping proper traceability information, consistency diagnostics obtained over the merge are projected back to the original models and their relationships. The paper also presents a set of reusable expressions for defining consistency constraints in conceptual modelling. We demonstrate the use of the developed expressions in the specification of consistency rules for class and ER diagrams, and i ∗ goal models. 1

Abstract. Many different types of models are used in various scientific and engineering fields, reflecting the subject matter and the kinds of understanding that is sought in each field. Conceptual modeling techniques in software and information systems engineering have in the past focused mainly on describing and analyzing behaviours and structures that are implementable in software. As software systems become ever more complex and densely intertwined with the human social environment, we need models that reflect the social characteristics of complex systems. This chapter reviews the approach taken by the i* framework, highlights its application in several areas, and outlines some open research issues. 1 Why Social Modeling In many scientific and engineering disciplines, the principles, premises, and objectives of the field are embedded in and manifested through the models that are the daily conceptual tools of the profession. The models reflect the kinds of understanding that is sought by practitioners of the field. In software and information

Abstract. A considerable amount of effort has been placed into the investigation of i * modeling as a tool for early stage requirements engineering. However, widespread adoption of i * models in the requirements process has been hindered by issues such as the effort required to create the models, coverage of the problem context, and model complexity. In this work, we explore the feasibility of pattern application to address these issues. To this end, we perform both an exploratory case study and initial experiment to investigate whether the application of patterns improves aspects of i * modeling. Furthermore, we develop a methodology which guides the adoption of patterns for i * modeling. Our findings suggest that applying model patterns can increase model coverage, but increases complexity, and may increase modeling effort depending on the experience of the modeler. Our conclusions indicate situations where pattern application to i * models may be beneficial.

Abstract. Goal- and agent-oriented models have emerged as a way to capture stakeholder and organizational goals in a complex enterprise. The complexity of such models leads to a need for systematic procedures to enable users to evaluate and compare the alternative actions and solutions expressed in models. Many existing approaches focus on automated procedures, limiting the ability of the user to intervene. Here, we introduce a qualitative, interactive evaluation procedure for goal- and agent-oriented models, allowing the modeler to supplement the evaluation with domain knowledge not captured in the model. We provide a sample methodology to guide model creation and domain exploration which includes the evaluation of alternatives. We illustrate the procedure and methodology with the i * Framework. Case study experience shows that the procedure facilitates analysis, prompts iteration over model development, promotes elicitation, and increases domain understanding. We describe the results of an exploratory experiment designed to test these findings. 1

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

Abstract. The early stage of domain analysis in requirements engineering is critical for understanding the stakeholders, their needs, problems, and how views of these problems differ. We advocate methods for early domain exploration which provoke iteration over captured knowledge, prompting analysts and stakeholders to review what is known, helping to guide elicitation, and facilitating early scoping and decision making. Specifically, we provide a framework to support interactive, iterative analysis over goal- and agentoriented (agent-goal) models. The framework will allow for multiple types of analysis questions, manage alternative evaluations over a model, manage interactive results, capture model assumptions and arguments, and support iteration over all constructs. Initial case study experience shows that interactive evaluation provokes model iteration and domain exploration. Further case studies will be developed to test the benefits of framework expansions. Keywords: Goal-and Agent-Oriented Models, Early RE, Model Analysis 1

Abstract. Goal decomposition structures lie at the heart of goal modeling languages such as i*. High-level goals of stakeholders are recursively decomposed into lower level ones and eventually into leaf level tasks to be performed by agents. The decomposition structure can also develop through a bottom up approach whereby higher-level goals are introduced as justifications for existing low-level ones. The very concept of decomposition, however, both as process and as artefact is rarely questioned in requirements engineering. In this paper, we argue that it may be of value to give a closer look into goal decomposition and clarify what we actually know about it and what is yet to be understood. We report on an on-going effort to identify empirical work on decomposition coming from various research fields, hoping to find such evidence. We then pose some research questions that we believe need to be pursued in order to improve our understanding of goal decomposition. Key words: requirements engineering, goal modeling, i-star 1