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Analyzing software measurement data with clustering techniques

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3 Author(s)
S. Zhong ; Dept. of Comput. Sci., Florida Atlantic Univ., Boca Raton, FL, USA ; T. M. Khoshgoftaar ; N. Seliya

For software quality estimation, software development practitioners typically construct quality-classification or fault prediction models using software metrics and fault data from a previous system release or a similar software project. Engineers then use these models to predict the fault proneness of software modules in development. Software quality estimation using supervised-learning approaches is difficult without software fault measurement data from similar projects or earlier system releases. Cluster analysis with expert input is a viable unsupervised-learning solution for predicting software modules' fault proneness and potential noisy modules. Data analysts and software engineering experts can collaborate more closely to construct and collect more informative software metrics.

Published in:

IEEE Intelligent Systems  (Volume:19 ,  Issue: 2 )