Dependency-aware software requirements selection using fuzzy graphs and integer programming

One of the critical activities in software development is Requirements Selection, which to find an optimal subset requirements (features) with highest value for a given budget. The values requirements, however, may depend on one another. Such Value Dependencies have not been considered by existing selection methods, leading user dissatisfaction and loss reputation projects. To mitigate this, we propose Dependency-Aware Selection (DARS) as expert system, explicitly accounts dependencies At heart DARS Integer Linear Programming (ILP) model that reduces risk considering among requirements. These are identified from preferences users validly verified studying real-world project well carrying out simulations. Our results demonstrate significant reduction when employed. Also, ILP proved scalable large requirement sets (experimented up 3000). our study can be extrapolated wide range systems concern selecting value-dependent items.