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Issue No. 02 - March/April (2004 vol. 19)
ISSN: 1541-1672
pp: 20-27
Shi Zhong , Florida Atlantic University
Naeem Seliya , Florida Atlantic University
Taghi M. Khoshgoftaar , Florida Atlantic University
<p>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.</p>
software quality estimation, exploratory data analysis, clustering, noise detection
Shi Zhong, Naeem Seliya, Taghi M. Khoshgoftaar, "Analyzing Software Measurement Data with Clustering Techniques", IEEE Intelligent Systems, vol. 19, no. , pp. 20-27, March/April 2004, doi:10.1109/MIS.2004.1274907
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