  The Next Release Problem consists in selecting a subset of requirements to
develop in the next release of a software product. The selection should be done
in a way that maximizes the satisfaction of the stakeholders while the
development cost is minimized and the constraints of the requirements are
fulfilled. Recent works have solved the problem using exact methods based on
Integer Linear Programming. In practice, there is no need to compute all the
efficient solutions of the problem; a well-spread set in the objective space is
more convenient for the decision maker. The exact methods used in the past to
find the complete Pareto front explore the objective space in a lexicographic
order or use a weighted sum of the objectives to solve a single-objective
problem, finding only supported solutions. In this work, we propose five new
methods that maintain a well-spread set of solutions at any time during the
search, so that the decision maker can stop the algorithm when a large enough
set of solutions is found. The methods are called anytime due to this feature.
They find both supported and non-supported solutions, and can complete the
whole Pareto front if the time provided is long enough.
