A Fuzzy-Based Optimization Method for Integrating Value Dependencies into Software Requirement Selection
This addresses the risk of value loss in software engineering for practitioners, but it is incremental as it builds on existing optimization methods.
The paper tackles the problem of software requirement selection by proposing a method that accounts for value dependencies between requirements, demonstrating through simulations that it effectively reduces the risk of value loss.
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.