The iStar 2.0 modeling language is the result of a two-year community effort intended at providing a solid, unified basis for teaching and conducting research with i*. The language was released with important qualities in mind, such as keeping a core set of primitives, providing a clear meaning for those primitives, and flattening the learning curve for new users. In this paper, we propose a list of qualities against which we intend iStar 2.0 to be evaluated. Furthermore, we describe an empirical evaluation plan, which we devise in order to assess the extent to which the language meets the identified qualities and to inform the development of further versions of the language. Besides explaining the objectives and steps of our planned empirical studies, we make a call for involving the research community in our endeavor.

Dealing with multiple requirement failures is an essential capability for self-adaptive software systems. This capability becomes more challenging in the presence of conflicting goals. This paper is concerned with the next adaptation problem: the problem of finding the best next adaptation in the presence of multiple failures. ‘Best’ here means that the adaptation chosen optimizes a given set of objective functions, such as the cost of adaptation or the degree of failure for system requirements. The paper proposes a formal framework for defining the next adaptation problem, assuming that we can specify quantitatively the constraints that hold between indicators that measure the degree of failure of each requirement and control parameters. These constraints, along with one or several objective functions, are translated into a constrained multi-objective optimization problem that can be solved by using an OMT/SMT (Optimization Modulo Theories/Satisfiability Modulo Theories) solver, such as OptiMathSAT. The proposed framework is illustrated with the Meeting Scheduler exemplar and a second, e-shop case study.

Risks of software projects are often ignored and risk analysis is left for later stages of project life-cycle, resulting in serious financial losses. This paper proposes a goal-oriented risk analysis framework that includes inter-dependencies among treatments and risks in terms of likelihood and generate optimal solutions with respect to multiple objectives such as goal rewards, treatment costs, or risk factor. The Loan Origination Process illustrates our approach and a detailed analysis of the visual notation is provided.

In this paper we present Next Release Tool (NRT), a software tool that supports modeling and reasoning to solve the next release problem. NRT supports a goal-oriented modeling language and uses an underlying satisfiability modulo theorem (SMT) solver with optimization capabilities.

Modeling languages have been evaluated through empirical studies, comparisons of language grammars, and ontological analyses. In this paper we take the first approach, evaluating the expressiveness and effectiveness of Techne, a requirements modeling language, by applying it to three requirements problems from the literature. We use our experiences to propose a number of language improvements for Techne, addressing challenges discovered during the studies. This work presents an example evaluation of modeling language expressiveness and effectiveness through realistic case studies.

We address the challenge of requirements engineering for sociotechnical systems, wherein humans and organizations supported by technical artifacts such as software interact with one another. Traditional requirements models emphasize the goals of the stakeholders above their interactions. However, the participants in a sociotechnical system may not adopt the goals of the stakeholders involved in its specification. We motivate, Protos, a requirements engineering approach that gives prominence to the interactions of autonomous parties and specifies a sociotechnical system in terms of its participants’ social relationships, specifically, commitments. The participants can adopt any goal they like, a key basis for innovative behavior, as long as they interact according to the commitments. Protos describes an abstract requirements engineering process as a series of refinements that seek to satisfy stakeholder requirements by incrementally expanding a specification set and an assumption set, and reducing requirements until all requirements are accommodated. We demonstrate this process via the London Ambulance System described in the literature.

Sociotechnial systems (STSs) consist of a complex interplay of technical components, humans, and organizations. As other types of systems, STSs need to evolve in response to changing requirements and operational environments. Evolving STSs is a complex activity, which requires reconfiguration of technical components as well as rerouting of interactions among human and social actors. Moreover, reconfiguration has to respect participant autonomy, while coping with conflicting goals and noncooperation in identifying a configuration that minimizes changes relative to the current configuration. In this paper, we present a framework that supports design and evolution of STSs. The framework includes (i) the DEST language for modeling STSs as goal-oriented actors that interact via social commitments; (ii) techniques for building a network of interactions that fulfills participant requirements; and (iii) techniques for evolving an existing STS while minimizing change. We encode the design and evolution of STSs as an automated planning problem.

Developing and corroborating plans for critical missions (e.g., space missions) is not a trivial task. Our research focuses on designing and evaluating a planning tool that supports a team of experts engaged in sharing, cross-validating, and refining existing plans. We designed a plan visualization tool to support multi-team collaborative planning for operations on the International Space Station. We built and evaluated two prototypes, a text-based tool representing the current practice and a new, dual timeline visualization tool. In this paper we present the design and the formative evaluation, which indicated the promise of the proposed design but also identified implementation issues.

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in its flexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points.