Minimizing the stakeholder dissatisfaction risk in requirement selection for next release planning

The requirements to be delivered in the next software release are selected according to the stakeholders’ perceived value, expected implementation cost, budget availability, and precedence and technical dependency constraints. Existing approaches to the requirement selection problem do not take into...

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Bibliographic Details
Published in:	Information and software technology Vol. 87; pp. 104 - 118
Main Authors:	Pitangueira, A.M., Tonella, P., Susi, A., Maciel, R.S.P., Barros, M.
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01-07-2017
Subjects:	Multi-stakeholder Next release problem Risk-aware decision making Next release problem Risk-aware decision making Multi-stakeholder
Online Access:	Get full text
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Summary:	The requirements to be delivered in the next software release are selected according to the stakeholders’ perceived value, expected implementation cost, budget availability, and precedence and technical dependency constraints. Existing approaches to the requirement selection problem do not take into account the risk of stakeholders’ dissatisfaction possibly resulting from divergence in the stakeholders’ estimates of the requirement value. We present a novel risk-aware, multi-objective approach to the next release problem that aims at reducing the stakeholder dissatisfaction risk in a given cost/value region of interest provided by stakeholders. We have devised an exact algorithm to address the risk-aware formulation of the next release problem and implemented the algorithm using two well-known SMT solvers, Yices and Z3. To allow the application of the proposed formulation to large size problems, we have also implemented an approximate algorithm based on the NSGA-II metaheuristic. Results show that (1) the stakeholder dissatisfaction risk can be minimised with minimum impact on cost/value, and (2) our approach is scalable when NSGA-II is used. SMT solvers scale up to problems that are not overly large in terms of the number of requirements and/or are not too sparse in terms of dependencies, but the metaheuristic can quickly find good solutions even for large size problems. We recommend the users of our approach to apply an SMT solver and to resort to a metaheuristic algorithm only if the SMT solver does not terminate within reasonable time, due to the actual combination of number of requirements and dependency density.
ISSN:	0950-5849 1873-6025
DOI:	10.1016/j.infsof.2017.03.001