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.
The authors of @cite_6 also proposed StakeRare' @cite_7 which used social networks and collaborative filtering for large scale requirements. The paper addressed three problems for large scale projects: information overload, inadequate stakeholder input, and biased prioritization of requirements. The authors collected stakeholder list by eliciting requirements and deriving influence of stakeholders using interviews and the importance of each requirement was determined. From that, requirements were analyzed and a list of prioritized requirements were generated. Although the method performed well compared to other existing methods, it did not cover rank reversal problem.
@cite_7 @cite_6
1995790095 281966469 2147295172 2148933855
Requirements elicitation is the software engineering activity in which stakeholder needs are understood. It involves identifying and prioritizing requirements-a process difficult to scale to large software projects with many stakeholders. This paper proposes StakeRare, a novel method that uses social networks and collaborative filtering to identify and prioritize requirements in large software projects. StakeRare identifies stakeholders and asks them to recommend other stakeholders and stakeholder roles, builds a social network with stakeholders as nodes and their recommendations as links, and prioritizes stakeholders using a variety of social network measures to determine their project influence. It then asks the stakeholders to rate an initial list of requirements, recommends other relevant requirements to them using collaborative filtering, and prioritizes their requirements using their ratings weighted by their project influence. StakeRare was evaluated by applying it to a software project for a 30,000-user system, and a substantial empirical study of requirements elicitation was conducted. Using the data collected from surveying and interviewing 87 stakeholders, the study demonstrated that StakeRare predicts stakeholder needs accurately and arrives at a more complete and accurately prioritized list of requirements compared to the existing method used in the project, taking only a fraction of the time. 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. 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. This paper studies result diversification in collaborative filtering. We argue that the diversification level in a recommendation list should be adapted to the target users' individual situations and needs. Different users may have different ranges of interests -- the preference of a highly focused user might include only few topics, whereas that of the user with broad interests may encompass a wide range of topics. Thus, the recommended items should be diversified according to the interest range of the target user. Such an adaptation is also required due to the fact that the uncertainty of the estimated user preference model may vary significantly between users. To reduce the risk of the recommendation, we should take the difference of the uncertainty into account as well. In this paper, we study the adaptive diversification problem theoretically. We start with commonly used latent factor models and reformulate them using the mean-variance analysis from the portfolio theory in text retrieval. The resulting Latent Factor Portfolio (LFP) model captures the user's interest range and the uncertainty of the user preference by employing the variance of the learned user latent factors. It is shown that the correlations between items (and thus the item diversity) can be obtained by using the correlations between latent factors (topical diversity), which in return significantly reduce the computation load. Our mathematical derivation also reveals that diversification is necessary, not only for risk-averse system behavior (non-adpative), but also for the target users' individual situations (adaptive), which are represented by the distribution and the variance of the latent user factors. Our experiments confirm the theoretical insights and show that LFP succeeds in improving latent factor models by adaptively introducing recommendation diversity to fit the individual user's needs.