A recommender system for requirements elicitation in large-scale software projects

Abstract

In large and complex software projects, the knowledge needed to elicit requirements and specify the functional and behavioral properties can be dispersed across many thousands of stakeholders. Unfortunately traditional requirements engineering techniques, which were primarily designed to support face-to-face meetings, do not scale well to handle the needs of larger projects. We therefore propose a semi-automated requirements elicitation framework which uses data-mining techniques and recommender system technologies to facilitate stakeholder collaboration in a large-scale, distributed project. Our proposed recommender model is a hybrid one designed to manage the placement of stakeholders into highly focused discussion forums, where they can work collaboratively to generate requirements. In our approach, statements of need are first gathered from the project stakeholders; unsupervised clustering techniques are then used to identify cohesive and finely-grained themes and a users' profile is constructed according to the interests of the stakeholders in each of these themes. This profile feeds information to a collaborative recommender, which predicts stakeholders' interests in additional forums. The validity and effectiveness of the proposed recommendation framework is evaluated through a series of experiments using feature requests from three software systems.

On- and Off-Topic Classification and Semantic Annotation of User-Generated Software Requirements

“…Researchers developed (semi-) automated and collaborative approaches to support requirement engineers in this process (Ankori and Ankori, 2005;Castro-Herrera et al, 2009). Besides the elicitation in interaction with the users, an identification of requirements from existing sources is possible.…”

A Survey on Feature Recommendation Techniques

“…K-Nearest Neighbor (kNN) learning strategy perform well in forum recommendations [38,41]. Two wellknown methods are also used for recommendation against the Standard KNN i.e.…”

Speculative analysis of integrated development environment recommendations

“…5 We used a within-participants mixed design. We considered two factors: the tool or treatment factor (Quick Fix vs. Quick Fix Scout), and the task factor (α vs. β task sets).…”