loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
21st IEEE International Conference on Software Maintenance (ICSM'05)
Maintainability Prediction: A Regression Analysis of Measures of Evolving Systems
Budapest, Hungary
September 25-September 30
ISBN: 0-7695-2368-4
Jane Huffman Hayes, University of Kentucky
Liming Zhao, University of Kentucky
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.
Citation:
Jane Huffman Hayes, Liming Zhao, "Maintainability Prediction: A Regression Analysis of Measures of Evolving Systems," icsm, pp.601-604, 21st IEEE International Conference on Software Maintenance (ICSM'05), 2005
Usage of this product signifies your acceptance of the Terms of Use.