Title page for ETD etd-06112009-063248


Type of Document Master's Thesis
Author Love, Randall James
URN etd-06112009-063248
Title Predictive software design measures
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Nance, Richard E. Committee Chair
Arthur, James D. Committee Member
Henry, Sallie M. Committee Member
Keywords
  • Computer software
Date of Defense 1994-10-05
Availability restricted
Abstract

This research develops a set of predictive measures enabling software testers and designers to identify and target potential problem areas for additional and/or enhanced testing. Predictions are available as early in the design process as requirements allocation and as late as code walk-throughs. These predictions are based on characteristics of the design artifacts prior to coding.

Prediction equations are formed at established points in the software development process called milestones. Four areas of predictive measurement are examined at each design milestone for candidate predictive metrics. These areas are: internal corrlplexity, information flow, defect categorization, and the change in design. Prediction equations are created from the set of candidate predictive metrics at each milestone. The most promising of the prediction equations are selected and evaluated. The single "best" prediction equation is selected at each design milestone.

The resulting predictions are promising in terms of ranking areas of the software design by the number of predicted defects. Predictions of the actual number of defects are less accurate.

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