  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.
