Abstract
Software Product Line (SPL) is used to represent similar systems with multiple variants. Software product line plays a big role in minimizing cost, utilization of resource and maximizes chances to achieve the objective(s). Feature model is used to represent SPL. In our paper, the genetic algorithm is applied for feature optimization in software product line. We have designed and assessed our method by using it on an application with many different and complex dependencies among features. Further, we have also, optimized the feature model for those features that are highly used, are backbone functionality of software application and contribute to a larger extent in terms of customer attraction, satisfaction, iteration and retention. We have reported our experiment results against analysing 10 feature models with small set to large sets of features.
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