Back to results

Virginia Tech

Predicting maintainability with software quality metrics

Abstract

dc:description.abstract

Maintenance of software makes up a large fraction of the time and money spent in the software life cycle. By reducing the need for maintenance these costs can also be reduced. Predicting where maintenance is likely to occur can, help to reduce maintenance by prevention. This thesis details a study of the use of software quality;metrics to determine high complexity components in a software system. By the use of a history of maintenance done on a particular system, it is shown that a predictor equation can be developed to identify components which needed maintenance activities. This same equation can also be used to determine which components are likely to need maintenance in the future. Through the use of.these predictions and software metric complexities it should be possible to reduce the likelihood of a component needing maintenance. This might be accomplished by reducing the complexity of that component through further decomposition.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Computer Science and Applications
Department dc:contributor.department
Computer Science and Applications
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
1988

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Wake, Steven A.
Chair dc:contributor.committeechair
  • Henry, Sallie M.
Committee members dc:contributor.committeemember
  • Kafura, Dennis G.
  • Hartson, H. Rex

Rights

dc:rights
Statement dc:rights
  • In Copyright
Language dc:language.iso
en

Identifiers

dc:identifier.*
Dc Identifier Other
etd-06102012-040345
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/43067

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Wake, Steven A.. Predicting maintainability with software quality metrics. masters thesis, Virginia Tech, 1988. http://hdl.handle.net/10919/43067