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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
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
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