A collaborative filtering based persona identification in requirements elicitation

Persona is a fictional character that archetypically represents a user group. Persona identification is an important step in requirements elicitation. A review of related literature has shown that the persona is identified using qualitative approaches such as ethnographic profiling, user observations and user interviews. These approaches classify users on the basis of demographics or behavioral patterns. The drawbacks for such qualitative approaches are: they focus on detailed information gathering rather than correctly identifying representative user of persona; identified personas are too subjective as different requirements analysts may create different personas; these approaches do not scale well for a large number user involvement due to the high computational complexity of processing unstructured data. This paper proposed the collaborative filtering based persona-scenario (CFPS) approach to identify persona by calculating the similarities between the representative user to other users, combining the collaborative filtering algorithm and the persona-scenario approach. The case study shows the proposed approach improves the efficiency and accuracy in persona identification and requirements elicitation

Social Networks and Collaborative Filtering for Large-Scale Requirements Elicitation

Within the field of software engineering, requirements elicitation is the activity in which stakeholder needs are understood. In large-scale software projects, requirements elicitation tends to be beset by three problems: information overload, inadequate stakeholder input, and biased prioritisation of requirements. The work described in this thesis addresses these problems using social networks and collaborative filtering. The work has developed StakeNet, a novel method that uses social networks to identify and prioritise stakeholders. Using StakeNet, the requirements engineer asks an initial list of stakeholders to recommend other stakeholders and stakeholder roles, builds a social network with stakeholders as nodes and their recommendations as links, and prioritises the stakeholders using a variety of social network measures. The work has also developed StakeRare, a novel method that uses social networks and collaborative filtering to identify and prioritise requirements. Using StakeRare, the requirements engineer asks the stakeholders identified by StakeNet to rate an initial list of requirements and suggest other requirements, recommends other relevant requirements to the stakeholders using collaborative filtering, and prioritises the requirements using the ratings and the stakeholders’ priority from StakeNet. Finally, to support the methods, this work has developed StakeSource, a novel software tool that automates the manual processes in StakeNet. StakeSource collects recommendations from stakeholders, builds the social network, and prioritises the stakeholders automatically. The methods and tool have been evaluated using real large-scale software projects. The empirical evaluation of both StakeNet and StakeRare using a real large-scale software project demonstrates that the methods identify a highly complete set of stakeholders and their requirements, and prioritise the stakeholders and their requirements accurately. These methods outperform the existing methods used in the project, and require significantly less time from the stakeholders and requirements engineers. StakeSource has been evaluated with real large-scale projects by practitioners. The tool is now used in major software projects, and organisations are adopting it. The methods, tool, and evaluation described in this thesis provide evidence that social networks and collaborative filtering can effectively support requirements elicitation in large-scale software projects