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Title: Clustering methods for requirements selection and optimisation
Author: Veerappa, V.
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2013
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Decisions about which features to include in a new system or the next release of an existing one are critical to the success of software products. Such decisions should be informed by the needs of the users and stakeholders. But how can we make such decisions when the number of potential features and the number of individual stakeholders are very large? This problem is particularly important when stakeholders’ needs are gathered online through the use of discussion forums and web-based feature request management systems. Existing requirements decision-making techniques are not adequate in this context because they do not scale well to such large numbers of feature requests or stakeholders. This thesis addresses this problem by presenting and evaluating clustering methods to facilitate requirements selection and optimization when requirements preferences are elicited from a very large number of stakeholders. Firstly, it presents a novel method for identifying groups of stakeholders with similar preferences for requirements. It computes the representative preferences for the resulting groups and provides additional insights in trends and divergences in stakeholders’ preferences which may be used to aid the decision making process. Secondly, it presents a method to help decision-makers identify key similarities and differences among large sets of optimal design decisions. The benefits of these techniques are demonstrated on two real-life projects - one concerned with selecting features for mobile phones and the other concerned with selecting requirements for a rights and access management system.
Supervisor: Letier, E. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available