An Estimation of Distribution Algorithm based on interactions between requirements to solve the bi-objective Next Release Problem

TL;DR

This paper investigates the impact of requirements interactions on solving the bi-objective Next Release Problem, demonstrating that including interactions improves search performance and that EDAs are effective under time constraints.

Contribution

It introduces an estimation of distribution algorithm that explicitly incorporates requirements interactions for the bi-objective Next Release Problem, showing improved solution quality.

Findings

Interactions inclusion enhances search effectiveness.

EDAs reliably find diverse solutions under time constraints.

Explicit interaction modeling benefits bi-objective requirement selection.

Abstract

Selecting the appropriate requirements to develop in the next release of an open market software product under evolution, is a compulsory step of each software development project. This selection should be done by maximizing stakeholders' satisfaction and minimizing development costs, while keeping constraints. In this work we investigate what is the requirements interactions impact when searching for solutions of the bi-objective Next Release Problem. In one hand, these interactions are explicitly included in two algorithms: a branch and bound algorithm and an estimation of distribution algorithm (EDA). And on the other, we study the performance of these not previously used solving approaches by applying them in several instances of small, medium and large size data sets. We find that interactions inclusion do enhance the search and when time restrictions exists, as in the case of the bi-objective Next Release Problem, EDAs have proven to be stable and reliable locating a large number of solutions on the reference Pareto front.