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.

Abstract. The requirements for a system are often specified as textual use cases. Although they are written in natural language, the simple and uniform sentence structure used makes automated processing of use cases feasible. However, the numerous use case approaches vary in the permitted complexity and variations of sentence structure. Frequently, use cases are written in the form of compound sentences describing several actions. While there are methods for analyzing use cases following the very simple SVDPI (subjectverb-direct object... indirect object) pattern, methods for more complex sentences are still needed. We propose a new method for processing textual requirements based on the scheme earlier described in [13]. The new method allows to process the commonly used complex sentence structures, obtaining more descriptive behavior specifications, which may be used to verify and validate requirements and to derive the initial design of the system. 1

“Take pride in how far you have come; have faith in how far you can go. ” —Anonymous In the 21st century, AI has many reasons to be proud, but it wasn’t always this way. New technologies such as AI typically follow the hype curve (see Figure 1 1). By the mid-1980s, early successes with expert systems 2–5 caused skyrocketing attendance at AI conferences (see Figure 2) and a huge boom in North American AI startups. Just like the dot-coms in the late 1990s, this AI boom was characterized by unrealistic expectations. When the boom went bust, the field fell into a trough of disillusionment that Americans call the AI Winter. A similar disillusionment had already struck earlier, elsewhere (see the “Comments on the Lighthill Report” sidebar). If a technology has something to offer, it won’t stay in the trough of disillusionment, just as AI has risen to a new sustainable level of activity. For example, Figure 2 shows that although AI conference attendance numbers have been stable since 1995, they are nowhere near the unsustainable peak of the mid-1980s. With this special issue, I wanted to celebrate and record modern AI’s achievements and activity. Hence, the call for papers asked for AI’s current trends and historical successes. But the best-laid plans can go awry. It turns out that my “coming of age ” special issue was about five to 10 years too late. AI is no longer a bleeding-edge technology—hyped by its proponents and mistrusted by the mainstream. In the 21st century, AI is not necessarily amazing. Rather, it’s often routine. A maturing technology Evidence for AI technology’s routine and dependable nature abounds. For example, in this issue (see the related sidebar for a full list), authors describe various tools to augment standard software engineering:

A key problem in model-based development is merging a set of distributed models into a single seamless model. To merge a set of models, we need to know how they are related. In this position paper, we discuss the methodological aspects of describing the relationships between models. We argue that relationships between models should be treated as first-class artifacts in the merge problem and propose a general framework for model merging based on this argument. We illustrate the usefulness of our framework by instantiating it to the state-machine modelling domain and developing a flexible tool for merging state-machines.

In large-scale model-based development, developers periodically need to combine collections of interrelated models. These models may capture different features of a system, describe alternative perspectives on a single feature, or express ways in which different features may alter one another’s structure or behaviour. We refer to the process of combining a set of interrelated models as model fusion. In this position paper, we provide an overview of our work on two key fusion activities, merging and composition, for behavioural models. The practical basis of our work comes from two case studies that we conducted using models from the telecommunications domain. We illustrate our work using these case studies, summarize the results our research has led to so far, and describe the future research challenges.

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

Requirements evolution captures information about how software systems change as a result of changes in requirements. The goal of this project is to identify how existing requirements engineering methods, tools and models can be extended to to support the analysis of requirements evolution. It is hoped that by providing a tool for analyzing requirements evolution, our work will spur further research on requirements evolution, thereby enriching the field of requirements engineering. We present a design which supports requirements evolution analysis within conventional requirement engineering processes by building on the models proposed by researchers. We implement this design as a plug-in for the Eclipse IDE. Our plug-in supports requirement analysis by providing visualizations as means of presenting requirement changes and evolution. i Acknowledgements In delivering this thesis, I have been fortunate enough to have been assisted by many individuals whom I wish to acknowledge here. First and foremost, a heart-felt note of thanks to Massimo Felici, my thesis supervisor, for his generosity in dispensing

To my spouse and my mother, to all those who helped me, influenced me, or endured me throughout all these years, I express my profound gratitude. El Mostapha