Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Gaining Confidence in Software Inspection Using a Bayesian Belief Model
Software Quality Control
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Gaining confidence in the software development process using expert systems
SAFECOMP'06 Proceedings of the 25th international conference on Computer Safety, Reliability, and Security
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One of the main concerns in safety critical software development is to identify a path through the software development lifecycle that will allow the software artefact to meet the target safety integrity level (SIL) at an acceptable cost. In our previous work we modelled aspects of the software development process recommended by IEC61508-3 software safety standard. In general, there are a number of paths that one can follow in order to comply with a target SIL. The path that one chooses to follow will undoubtedly effect the costs of the software development. In this paper we study a series of optimization algorithms that can be used to improve the software development process by optimization of two objectives, development costs and confidence in claimable integrity. Our analyses show that the non-dominated sorting genetic algorithm (NSGA) is the best performing algorithm in the search for these optimal processes.