Title page for ETD etd-0929103-120652

Type of Document Dissertation
Author O'Neal, James Steven
Author's Email Address joneal@csc.lsu.edu
URN etd-0929103-120652
Title Analyzing the Impact of Changing Software Requirements: A Traceability-Based Methodology
Degree Doctor of Philosophy (Ph.D.)
Department Computer Science
Advisory Committee
Advisor Name Title
Doris L. Carver Committee Chair
Donald Kraft Committee Member
S. Sitharama Iyengar Committee Member
Yiping Lou Committee Member
Subhash C. Kak Dean's Representative
  • software engineering
  • fuzzy sets
  • change management
  • requirements traceability
  • impact analysis
Date of Defense 2003-09-24
Availability unrestricted
Software undergoes change at all stages of the software development process.

Changing requirements represent risks to the success and completion of a

project. It is critical for project management to determine the impact of

requirement changes in order to control the change process. We present a

requirements traceability based impact analysis methodology to predictively

evaluate requirement changes for software development projects. Trace-based

Impact Analysis Methodology (TIAM) is a methodology utilizing the trace

information, along with attributes of the work products and traces, to

define a requirement change impact metric for determining the severity of a

requirement change. We define the Work product Requirements trace Model

(WoRM) to represent the information required for the methodology, where WoRM

consists of the models Work product Information Model (WIM) for the software

product and Requirement change Information Model (RIM) for requirement

changes. TIAM produces a set of classes of requirement changes ordered from

low to high impact. Requirement changes are placed into classes according

their similarity. The similarity between requirement changes is based on a

fuzzy compatibility relation between their respective requirement change

impact metrics. TIAM also identifies potentially impacted work products by

generating a set of potentially impacted work products for each requirement

change. The experimental results show a favorable comparison between classes

of requirement changes based on actual impact and the classes based on

predicted impact.

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