  Due to dynamic nature of current software development methods, changes in
requirements are embraced and given proper consideration. However, this
triggers the rank reversal problem which involves re-prioritizing requirements
based on stakeholders' feedback. It incurs significant cost because of time
elapsed in large number of human interactions. To solve this issue, a
Semi-Automated Framework for soFtware Requirements priOritizatioN (SAFFRON) is
presented in this paper. For a particular requirement, SAFFRON predicts
appropriate stakeholders' ratings to reduce human interactions. Initially,
item-item collaborative filtering is utilized to estimate similarity between
new and previously elicited requirements. Using this similarity, stakeholders
who are most likely to rate requirements are determined. Afterwards,
collaborative filtering based on latent factor model is used to predict ratings
of those stakeholders. The proposed approach is implemented and tested on RALIC
dataset. The results illustrate consistent correlation, similar to state of the
art approaches, with the ground truth. In addition, SAFFRON requires 13.5-27%
less human interaction for re-prioritizing requirements.
