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A risk-driven multi-objective evolutionary approach for selecting software requirements

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Abstract

Exploring iterative and incremental approaches, current software projects define various delivery releases, which ought to be developed within budgetary constraints, but raising customers’ satisfaction as much as possible. In search based software engineering, such a problem is referred to as next release problem (NRP), in which the selection of software requirements for the next release is reformulated as an optimization problem. More recently, NRP proposals have evolved to a multi-objective perspective, providing non-dominated recommendations that balance the trade-off surface with respect to customers’ satisfaction and development cost. Despite key advancements in NRP proposals, most of them do not deal with software risks, which represent a vital aspect that can deeply impact on project cost and customers’ satisfaction. In such a direction, using multi-objective evolutionary algorithms (MOEAs), this paper proposes and evaluates a risk-driven systematic approach for the NRP problem, in which a risk analysis is incorporated for estimating the impact of software risks in development cost and customers’ satisfaction. By contrasting three different well-known MOEAs, empirical results based on two semi-real datasets reveal the potential efficiency and practical applicability, bringing not only more realistic, precise and meaningful estimations, but also finding a significant percentage of unanticipated solutions.

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  1. Informatics Center, Federal University of Paraíba, João Pessoa, PB, Brazil

    Aruan Amaral & Gledson Elias

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  1. Aruan Amaral
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Amaral, A., Elias, G. A risk-driven multi-objective evolutionary approach for selecting software requirements. Evol. Intel. 12, 421–444 (2019). https://doi.org/10.1007/s12065-019-00237-2

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