Maintainability prediction: A regression analysis of measures of evolving systems

Jane Huffman Hayes, Liming Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

34 Scopus citations

Abstract

In order to build predictors of the maintainability of evolving software, we first need a means for measuring maintainability as well as a training set of software modules for which the actual maintainability is known. This paper describes our success at building such a predictor. Numerous candidate measures for maintainability were examined, including a new compound measure. Two datasets were evaluated and used to build a maintainability predictor. The resulting model, Maintainability Prediction Model (MainPredMo), was validated against three held-out datasets. We found that the model possesses predictive accuracy of 83% (accurately predicts the maintainability of 83% of the modules), A variant of MainPredMo, also with accuracy of 83%, is offered for interested researchers.

Original languageEnglish
Title of host publicationProceedings of the 21st IEEE International Conference on Software Maintenance, ICSM 2005
Pages601-604
Number of pages4
DOIs
StatePublished - 2005
Event21st IEEE International Conference on Software Maintenance, ICSM 2005 - Budapest, Hungary
Duration: Sep 26 2005Sep 29 2005

Publication series

NameIEEE International Conference on Software Maintenance, ICSM
Volume2005

Conference

Conference21st IEEE International Conference on Software Maintenance, ICSM 2005
Country/TerritoryHungary
CityBudapest
Period9/26/059/29/05

ASJC Scopus subject areas

  • Software

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