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Finding Robust Solutions in Requirements Models
"... Abstract. Solutions to nonlinear requirements engineering problems may be “brittle”; i.e. small changes may dramatically alter solution effectiveness. Hence, it is not enough to just generate solutions to requirements problems we must also assess solution robustness. The KEYS2 algorithm can genera ..."
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Abstract. Solutions to nonlinear requirements engineering problems may be “brittle”; i.e. small changes may dramatically alter solution effectiveness. Hence, it is not enough to just generate solutions to requirements problems we must also assess solution robustness. The KEYS2 algorithm can generate decision ordering diagrams. Once generated, these diagrams can assess solution robustness in linear time. In experiments with realworld requirements engineering models, we show that KEYS2 can generate decision ordering diagrams in O(N 2). When assessed in terms of terms of (a) reducing inference times, (b) increasing solution quality, and (c) decreasing the variance of the generated solution, KEYS2 outperforms other search algorithms (simulated annealing, ASTAR, MaxWalkSat). 1
Minimal ContrastSet Learning, ModelBased Software Engineering, MetaHeuristic Search Abstract The Robust Optimization of NonLinear Requirements Models
, 2010
"... Solutions to nonlinear requirements engineering problems may be “brittle”; i.e. small changes may dramatically alter solution effectiveness. Hence, it is not enough to just generate solutions to requirements problems we must also assess solution robustness. This thesis aims to address two concerns ..."
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Solutions to nonlinear requirements engineering problems may be “brittle”; i.e. small changes may dramatically alter solution effectiveness. Hence, it is not enough to just generate solutions to requirements problems we must also assess solution robustness. This thesis aims to address two concerns: (a) Is demonstrating robustness a time consuming task? and (b) Is it necessary that solution quality be traded off against solution robustness? Using a Bayesian ranking heuristic, the KEYS2 algorithm fixes a small number of important variables, rapidly pushing the search into a stable, optimal plateau. By design, KEYS2 generates decision ordering diagrams (in time experimentally shown to be O(N2)). Once generated, these diagrams can confirm solution robustness in linear time. When assessed in terms of reducing inference times, increasing solution quality, and decreasing the variance of the generated solution, KEYS2 outperforms other search algorithms (simulated annealing, A*, MaxWalkSat).