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.
In @cite_23 , the authors presented quantitative framework for prioritizing nonfunctional requirements by using scenario-based approach. However, this approach fail to incorporate new requirements or change of existing ones and the evaluation suffered from validation issues. The research stated at @cite_10 introduced a multi-criteria decision making system- Requirements Prioritizer' to prioritize requirements from any location. The system, while scalable and addressed the rank reversal issues persistent in techniques @cite_22 such as AHP, bubble sort, case base rank etc., had one major shortcoming. The approach did not prioritize or categorize stakeholders based on different requirement knowledge.
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Multiple-criteria decision making (MCDM) is widely used in ranking choices from a set of available alternatives with respect to multiple criteria. To analytically rank requirements under various criteria, we propose a tool called requirements prioritizer (RP) which has the capacity of keeping records of project stakeholders with their relative weights against each requirement, utilized by the system to compute an ordered list of prioritized requirements. The proposed approach offers a novel way of involving stakeholders in the entire decision making process irrespective of their numbers in an automated fashion. In this proposed approach, the relative weights assigned by each stakeholder are normalized and aggregated. The output of the system consists of prioritized requirements with an automatically generated graph showing the relative values of requirements across project stakeholders in a chronological order. 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. Interviews with stakeholders can be a useful method for identifying user needs and establishing requirements. However, interviews are also problematic. They are time consuming and may result in insufficient, irrelevant or invalid data. Our goal is to re-examine the methodology of interview design, to determine how various contextual factors affect the success of interviews in requirements engineering. We present a case study of a Web conferencing system used by a support group for spousal caregivers of people with dementia. Two sets of interviews were conducted to identify requirements for a new version of the system. Both sets of interviews had the same information elicitation goals, but each used different interview tactics. A comparison of the participants' responses to each format offers insights into the relationship between the interview context and the relative success of each interview technique for eliciting the desired information. As a result of what we learned, we propose a framework to help analysts design interviews and chose tactics based on the context of the elicitation process. We call this the contextual risk analysis framework. The importance of candidate software requirements can vary by orders of magnitude, yet most software providers do not have accurate and efficient means for selecting among them. The paper describes a case study at Ericsson Radio Systems AB of two techniques for software requirements prioritizing as a means for determining the importance of candidate requirements, a pairwise comparison technique and a numeral assignment technique. The results from the case study indicate that the pairwise comparison technique is an efficient informative and accurate means for finding the candidate requirements importance. We therefore recommend the pairwise comparison technique for software requirements prioritizing. At Ericsson we have extended its use in other development projects.