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Over the years, the domain of Self-Adaptive Systems (SAS) has gained significant importance in software engineering community.

In our evaluation with various configuration models we show that the technique is feasible, saves configuration effort and scales for real problem sizes of a few hundreds of features. A genetic algorithm is employed to solve the MDP when problem sizes prevent use of common exact techniques.

A Markov decision process (MDP) model is then created to encode configuration elicitation dialogs that maximize the automatic configuration decisions based on the crowd and the designers’ information. Designers augment this information with their own constraints. A repository of existing user configurations supplies us with information about popular ways to complete an existing partial configuration.

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We introduce a technique for designing optimal interactive configuration elicitation dialogs, aimed at utilizing crowd and expert information to reduce the amount of manual configuration effort. Crowds of other users or the designers themselves can provide with examples and rules as to what constitutes a meaningful configuration. However, users are rarely alone in using a software system.

Users, especially those that are new to a system, are faced with an ever increasing number of configuration possibilities, making the task of choosing the right one more and more daunting. Our approach broadens the scope of requirements variability by weaving context variability with both stakeholder goals and preferences, in order to sufficiently represent the adaptability needs of software systems where contextual changes are commonplace.Īs software systems grow in size and complexity, the process of configuring them to meet individual needs becomes more and more challenging. We further demonstrate the use of our approach in detecting modelling errors and validating the impact of prioritizations, leading to improvements in the requirements specifications. To support our variability analysis, we develop a tool to automate the derivation and evaluation of the solutions. Then, from those applicable solutions, we use the contextual preferences as criteria for evaluating and selecting the ones that would best satisfy stakeholder priorities. Given a requirements problem represented through a goal model, we use the contextual goals to derive applicable solutions at a given situation. We define contextual goals and contextual preferences to specify the relationships of contexts with requirements and preferences, respectively. In this paper, we propose an approach for context-based requirements variability analysis in the goal-oriented requirements modelling. context variability, to requirements variability is necessary. Requirements variability motivates adaptability hence, understanding the influence of the domain changes, i.e. Modern software systems are continuously seeking for adaptability realizations, to generate better fit behaviours in response to domain changes. We evaluated the approach using an industrial case study which shows the applicability of the approach and a controlled experiment which shows the benefits of the approach. Our approach allows for exploring which goals can be fulfilled by which configurations and which goals cannot be fulfilled by these configurations. Hence, to provide support for reasoning about dependencies between different configurations and the goals they can achieve, this paper contributes an automated model-based reasoning approach using view generations. Due to the huge number of possible configurations and their various dependencies to the different goals of the network, it is infeasible to handle this amount of information manually. As networks of collaborative cyber-physical systems are of a dynamic nature, the composition of such a network can change during runtime, leading to a plethora of often similar, albeit slightly different configurations. its composition of partaking individual systems. However, which goals such a network can achieve depends, in part, on the networks current configuration, i.e. Networks of collaborative cyber-physical systems can achieve goals individual systems are incapable of achieving on their own.

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