Dissertation/Thesis Abstract

Enhancing requirements-level defect detection and prevention with visual analytics
by Rad, Shirin, M.S., Mississippi State University, 2014, 59; 1554996
Abstract (Summary)

Keeping track of requirements from eliciting data to making decision needs an effective path from data to decision [43]. Visualization science helps to create this path by extracting insights from flood of data. Model helps to shape the transformation of data to visualization. Defect Detection and Prevention model was created to assess quality assurance activities. We selected DDP and started enhancing user interactivity with requirements visualization over basic DDP with implementing a visual requirements analytics framework. By applying GQM table to our framework, we added six visualization features to the existing visual requirements visualization approaches. We applied this framework to technical and non-technical stakeholder scenarios to gain the operational insights of requirements-driven risk mitigation in practice. The combination of the first and second scenarios' result presented the multiple stakeholders scenario result which was a small number of strategies from kept tradespase with common mitigations that must deploy to the system.

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Indexing (document details)
Advisor: Niu, Nan
Commitee: Allen, Edward B., Dampier, David
School: Mississippi State University
Department: Computer Science and Engineering
School Location: United States -- Mississippi
Source: MAI 52/06M(E), Masters Abstracts International
Subjects: Computer Engineering, Computer science
Keywords: Defect Detection and Prevention, Goal question metric, Multiple stakeholders and actionable decisions, Quality assurance activities, Requirements-driven risks mitigations, Risk assessment, Visual requirements analytics
Publication Number: 1554996
ISBN: 9781303865398
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