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In software development projects, the process of requirements engineering (RE) is one in which requirements are elicited, analyzed, documented, and managed. Requirements are traditionally collected using manual approaches, including interviews, surveys, and workshops. Employing traditional RE methods to engage a large base of users has always been a challenge, especially when the process involves users beyond the organization’s reach. Furthermore, emerging software paradigms, such as mobile computing, social networks, and cloud computing, require better automated or semi-automated approaches for requirements elicitation because of the growth in systems users, the accessibility to crowd-generated data, and the rapid change of users’ requirements. This research proposes a methodology to capture and analyze crowd-generated data (e.g., user feedback and comments) to find potential requirements for a software system in use. It semi-automates some requirementselicitation tasks using data retrieval and natural language processing (NLP) techniques to extract potential requirements. It supports requirements engineers’ efforts to gather potential requirements from crowd-generated data on social networks (e.g., Twitter). It is an assistive approach that taps into unused knowledge and experiences emphasizing continuous requirements elicitation during systems use. Keywords—Requirements engineering; RE; crowd data mining; NLP; Twitter; continuous requirements elicitation

Crowd-Generated Data Mining for Continuous Requirements Elicitation