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 reprioritizing requirements.
Software requirements prioritization is the act of ranking user’s requirements in order to plan for release phases. The essence of prioritizing requirements is to avoid breach of contract, trust or agreement during software development process. This is crucial because, not all the specified requirements could be implemented in a single release due to inadequate skilled programmers, time, budget, and schedule constraints. Major limitations of existing prioritization techniques are rank reversals, scalability, ease of use, computational complexities and accuracy among others. Consequently, an innovative model that is capable of addressing these problems is presented. To achieve our aim, synthesized weights are computed for criteria that make up requirements and functions were defined to display prioritized requirements based on the global weights of attributes across project stakeholders. An empirical case scenario is described to illustrate the adaptability processes of the proposed approach.