SAFFRON: A Semi-Automated Framework for Software Requirements Prioritization

TL;DR

SAFFRON is a semi-automated framework that predicts stakeholder ratings to efficiently re-prioritize software requirements, reducing human interactions by up to 27% while maintaining accuracy.

Contribution

It introduces a novel semi-automated approach combining collaborative filtering techniques to minimize human effort in requirement prioritization.

Findings

Achieves similar accuracy to state-of-the-art methods.

Reduces human interactions by 13.5-27%.

Validated on RALIC dataset.

Abstract

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.