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 complexity, 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. / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/43141 |
| Date | 11 June 2009 |
| Creators | Love, Randall James |
| Contributors | Computer Science, Nance, Richard E., Arthur, James D., Henry, Sallie M. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis, Text |
| Format | xi, 164 leaves, BTD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | OCLC# 32180469, LD5655.V855_1994.L684.pdf |
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