MBRP: Model-Based Requirements Prioritization Using PageRank Algorithm

Abstract

Requirements prioritization plays an important role in driving project success during software development. Literature reveals that existing requirements prioritization approaches ignore vital factors such as interdependency between requirements. Existing requirements prioritization approaches are also generally time-consuming and involve substantial manual effort. Besides, these approaches show substantial limitations in terms of the number of requirements under consideration. There is some evidence suggesting that models could have a useful role in the analysis of requirements interdependency and their visualization, contributing towards the improvement of the overall requirements prioritization process. However, to date, just a handful of studies are focused on model-based strategies for requirements prioritization, considering only conflict-free functional requirements. This paper uses a meta-model-based approach to help the requirements analyst to model the requirements, stakeholders, and inter-dependencies between requirements. The model instance is then processed by our modified PageRank algorithm to prioritize the given requirements. An experiment was conducted, comparing our modified PageRank algorithm's efficiency and accuracy with five existing requirements prioritization methods. Besides, we also compared our results with a baseline prioritized list of 104 requirements prepared by 28 graduate students. Our results show that our modified PageRank algorithm was able to prioritize the requirements more effectively and efficiently than the other prioritization methods.

Evaluation of PC-FMEA Using Network Analysis

“…The weighted in-degree shows how many input edges a given node has and their weights [10]. The PageRank indicator also takes into account the role of these nodes in the network [11].…”

Application of Network Science to Extend the AHP and QFD Methods

“…[11]. A PageRank algorithm was also used to prioritize requirements, and it proved to be suitable for prioritization of customer needs [12] [13]. However, these applications did not focus on the replacing and extending the AHP and QFD methods.…”