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An Early Look at Defining Variability Requirements for System-of-Systems Platforms

Conference Paper
This paper describes the development of a method for analyzing decisions about requirements for common platforms for systems of systems to enable controlled evolution.



This paper was published by IEEE in the proceedings of the 2012 Second IEEE International Workshop on Requirements Engineering for Systems, Services, and Systems-of-Systems (RESS), Chicago, IL, 2012, pp. 30–33.

In the commercial domain, platform-based approaches, in which a set of functions or services are bundled to form the basis of many products, have enabled efficient development of systems and their composition into systems of systems. A successful platform must balance sufficient commonality to support economical reuse while also providing variability and extensibility to enable innovation in system and system-of-systems (SoS) capabilities. These commonality/variability tradeoffs for SoS platforms are frequently tacit decisions, since there are no accepted techniques for analyzing such decisions at the scale and degree of requirements uncertainty that characterize most SoSs. The objective of our work is to develop a method for analyzing decisions about requirements for common platforms for SoSs. The method begins with the requirements tasks of identifying and selecting appropriate variabilities (variation points, variation ranges, and variation decision binding times) to support immediate SoS needs and enable innovation and controlled evolution. We are currently conducting a workshop and interviews with SoS experts to define the essential technical problems in SoS common platform development and identify solution constraints. We will then define a simplified SoS with limited capability requirements to use as a model problem. We will use the model problem to assess the fit of existing scope, commonality, and variability methods from software product lines to the SoS context and extend existing economic models using real options and probabilistic models to model uncertainty in evolution requirements. While it is too early to draw firm conclusions about the effectiveness of our approach, it is based on proven technologies from the mature field of software product lines, so we have confidence that we can build successful SoS techniques from this foundation.