Document Type

Article

Publication Date

2019

Abstract

Software companies, that adopt agile methodologies in the development of a large and complex software product, encounter the problem of selecting the subset of requirements to be included in the next release of the product. This problem is known as the next release problem (NRP). NRP is identified as an NP-hard problem as it involves a set of conflicting objectives that need to be addressed. These objectives are as follows: (1) maximizing the customer satisfaction taking into consideration that each customer has an importance level to the company, and (2) minimizing the development cost such that it does not exceed the allocated budget. Furthermore, the requirements have dependency and precedence relationships, and each requirement has a priority for each customer according to the customer’s needs. Therefore, metaheuristic algorithms are good candidate for tackling this problem. This paper proposes a hybrid approach to address the multi-objective constrained NRP. The proposed approach is based on adapting an improved binary particle swarm optimization (IBPSO) algorithm. Additionally, a greedy methodology was utilized for swarm initialization to seed the swarm with good solutions. Experimental results, of over six small and large NRP instances, demonstrated that the proposed approach converges much faster to solutions.

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