REASSURE: Requirements elicitation for adaptive socio-technical systems using repertory grid

Context: Socio-technical systems are expected to understand the dynamics of the execution environment and behave accordingly. Significant work has been done on formalizing and modeling requirements of such adaptive systems. However, not enough attention is paid on eliciting requirements from users and introducing flexibility in the system behavior at an early phase of requirements engineering. Most of the work is based on an assumption that general users' cognitive level would be able to support the inherent complexity of variability acquisition. Objective: Our main focus is on providing help to the users with ordinary cognitive level to express their expectations from the complex system considering various contexts. This work also helps the designers to explore the design variability based on the general users' preferences. Method: We explore the idea of using a cognitive technique Repertory Grid (RG) to acquire knowledge from users and experts along multiple dimensions of problem and design space. We propose REASSURE methodology which guides requirements engineers to explore the intentional and design variability in an organized way. We also provide a tool support to analyze the knowledge captured in multiple repertory grid files and detect potential conflicts in the intentional variability. Finally, we evaluate the proposed idea by performing an empirical study using smart home system domain. Results: The result of our study shows that a greater number of requirements can be elicited after applying our approach. With the help of the provided tool support, it is even possible to detect a greater number of conflicts in user's requirements than the traditional practices. Conclusion: We envision RG as a technique to filter design options based on the intentional variability in various contexts. The promising results of empirical study open up new research questions: "how to elicit requirements from multiple stakeholders and reach consensus for multi-dimensional problem domain". (C) 2017 Elsevier B.V. All rights reserved.