A Recoverable Robust Approach for the Next Release Problem

Abstract

Selecting a set of requirements to be included in the next software release, which has become to be known as the Next Release Problem, is an important issue in the iterative and incremental software development model. Since software development is performed under a dynamic environment, some requirements aspects, like importance and effort cost values, are highly subject to uncertainties, which should be taken into account when solving this problem through a search technique. Current robust approaches for dealing with these uncertainties are very conservative, since they perform the selection of the requirements considering all possible uncertainties realizations. Thereby, this paper presents an evolution of this robust model, exploiting the recoverable robust optimization framework, which is capable of producing recoverable solutions for the Next Release Problem. Several experiments were performed over synthetic and real-world instances, with all results showing that the recovery strategy handles well the conservatism and adds more quality to the robust solutions.