A robust optimization approach to the next release problem in the presence of uncertainties

摘要

The next release problem is a significant task in the iterative and incremental software development model, involving the selection of a set of requirements to be included in the next software release. Given the dynamic environment in which modern software development occurs, the uncertainties related to the input variables of this problem should be taken into account. In this context, this paper presents a formulation to the next release problem considering the robust optimization framework, which enables the production of robust solutions. In order to measure the "price of robustness", which is the loss in solution quality due to robustness, a large empirical evaluation was executed over synthetical and real-world instances. Several next release planning situations were considered, including different number of requirements, estimating skills and interdependencies between requirements. All empirical results are consistent to show that the penalization with regard to solution quality is relatively small. In addition, the proposed model's behavior is statistically the same for all considered instances, which qualifies it to be applied even in large-scale real-world software projects.