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Efficient Software Reliability Prediction With Evolutionary Virtual Data Position Exploration
Abstract
Determining appropriate software reliability prediction technique is a challenging task for the software development process. So, it is essential for software engineers to develop good quality software product. Though several prediction models are in use for small size data, the estimation of the reliability of software system is crucial. Inadequate data may lead sub-optimal solution. This chapter proposes a technique of increasing training dataset by generating virtual data points original data. For improving the prediction of cumulative failure time in software, multilayer perceptron (MLP)-based virtual data positions (DEVDP) exploration techniques have been proposed. The parameters of the network are optimized by evolutionary algorithm differential evolution (DE). For validation of the model in presence of virtual data point (VDP), eight failure datasets from different sources has been used. The results obtained from the simulation studies indicate that proposed DEVDP exploration technique outperformed traditional models.
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