Abstract
Software evolution is critical to extending the utility and life of distributed real-time and embedded (DRE) systems. Determining the optimal set of software and hardware components to evolve that (1) incorporate cutting-edge technology and (2) satisfy DRE system resource constraints, such as memory, power, and CPU usage is an NP-Hard problem. This article provides four contributions to evolving legacy DRE system configurations. First, we present the Software Evolution Analysis with Resources (SEAR) technique for converting legacy DRE system configurations, external resource availabilities, and candidate replacement components into multiple-choice multidimension knapsack problems (MMKP). Second, we present a formal methodology for assessing the validity of evolved system configurations. Third, we apply heuristic approximation algorithms to determine low-cost, high value evolution paths in polynomial time. Finally, we analyze results of experiments that apply these techniques to determine which technique is most effective for given system parameters. Our results show that constraint solvers can only evolve small system configurations, whereas approximation techniques are needed to evolve larger system configurations.
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