Abstract
Nowadays, software has become a part of greater significance in human life. The chief goal in today’s environment is to prepare the software that can meet all the needs of stakeholders’ increase in complexity of software boost in the requirement too. Many requirements are fulfilled in the given time duration or can be considered first to reduce the risk by the proper utilization of the software. In this way, gathering and prioritizing requirements can help in the successive development of the software. Requirement prioritization is one of the essential decision-making processes. Its main objective is to extract the suitable facts from the client, describing the requirements by the attributes. The ultimate purpose is to minimize the user decision-making effort and to increase the accuracy of the final requirements’ ranking. A number of requirement prioritization techniques have been proposed till date but are not efficient performance wise. This piece of work presents a new technique which performs an enhancement in performance by using least-squares-based random genetic algorithm. The aim of this research is to assist engineers in requirement prioritization by reducing time and hence minimizing decision-making efforts. In the proposed work as the number of pairs increases, the distance is slightly increases. Select the initial population by analyzing some of the input partial orders, or it can be done randomly. The time also reduces. In future, to integrate genetic algorithm (GA) and value-oriented prioritization (VOP), take the benefit of both for the better outputs.
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Ahuja, H., Sujata, Batra, U. (2018). Performance Enhancement in Requirement Prioritization by Using Least-Squares-Based Random Genetic Algorithm. In: Panda, B., Sharma, S., Batra, U. (eds) Innovations in Computational Intelligence . Studies in Computational Intelligence, vol 713. Springer, Singapore. https://doi.org/10.1007/978-981-10-4555-4_17
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