  Software requirement selection aims to find an optimal subset of the
requirements with the highest value while respecting the budget. But the value
of a requirement may depend on the presence or absence of other requirements in
the optimal subset. Existing requirement selection methods, however, do not
consider Value Dependencies, thus increasing the risk of value loss. To address
this, we have proposed Dependency-Aware Requirement Selection (DARS) method
with two main components: (i) a fuzzy-based technique for identifying and
modeling value dependencies, and (ii) an Integer Programming model that takes
into account value dependencies in software requirement selection. We have
further, proposed an alternative optimization model for situations where
quantifying value dependencies is hard. The scalability of DARS and its
effectiveness in reducing the risk of value loss are demonstrated through
exhaustive simulations.
