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Cost-effective software evolution is critical to many Distributed Real-time and Embedded (DRE) systems. Selecting the lowest cost set of software components that meet DRE system resource constraints, such as total memory and available CPU cycles, is an NP-Hard problem. This paper provides three contributions to R&D on evolving software-intensive DRE systems. First, the authors present the Software Evolution Analysis with Resources (SEAR) technique that transforms component-based DRE system evolution alternatives into multidimensional multiple-choice knapsack problems. Second, they compare several techniques for solving these knapsack problems to determine valid, low-cost design configurations for resource constrained component-based DRE systems.. Third, they empirically evaluate the techniques to determine their applicability in the context of common evolution scenarios.
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