This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Analyzing Software Measurement Data with Clustering Techniques
March/April 2004 (vol. 19 no. 2)
pp. 20-27
Shi Zhong, Florida Atlantic University
Taghi M. Khoshgoftaar, Florida Atlantic University
Naeem Seliya, Florida Atlantic University

Software engineers often construct quality-estimation models, used to predict the fault-proneness of software modules, by training a classifier from labeled software metrics data. They often encounter two challenges: noisy data and a lack of fault-proneness labels in real-world projects. You can?t train a classifier without fault-proneness labels. The clustering exploratory analysis method addresses these two challenges and uses clustering algorithms with the help of a software engineering expert. This method is unsupervised because it doesn?t require labeled training data to predict software modules? fault-proneness. Two real-world case studies verify this clustering- and expert-based approach?s effectiveness in predicting both software modules? fault-proneness and potentially noisy modules.

Index Terms:
software quality estimation, exploratory data analysis, clustering, noise detection
Citation:
Shi Zhong, Taghi M. Khoshgoftaar, Naeem Seliya, "Analyzing Software Measurement Data with Clustering Techniques," IEEE Intelligent Systems, vol. 19, no. 2, pp. 20-27, March-April 2004, doi:10.1109/MIS.2004.1274907
Usage of this product signifies your acceptance of the Terms of Use.