Data for: Evaluating Different i*-based Approaches for Selecting Functional Requirements while Balancing and Optimizing Non-Functional Requirements: A Controlled Experiment

This dataset is associated with the study published in Information and Software Technology, Volume 106, February 2019, Pages 68-84. The study investigates the effectiveness of an alternative automated i* post-processed model for requirements engineering. The primary objective of the study was to provide empirical evidence that requirement engineers may perform better at selecting Functional Requirements (FRs) while optimizing and balancing Non-Functional Requirements (NFRs) using this post-processed model, compared to the original i* model.

The data in this dataset was used for the analyses presented in the publication and includes the inputs required to evaluate the performance of requirement engineers in selecting FRs and optimizing NFRs. It provides the basis for comparing the decision-making efficiency and accuracy in FR selection, as well as the optimization process for NFRs in both the original and automated post-processed i* models.