Benefits of applying clustering algorithms to finding change request patterns
Group change requests according to text similarity
From the developerWorks archives
Date archived: December 20, 2016 | First published: April 17, 2012
This article describes an approach for analyzing IBM Rational Team Concert change request patterns by applying machine learning techniques, specifically clustering algorithms, to group the change requests according to text similarity. By performing this analysis, software development projects benefit in quality improvement, reuse, process, and team collaboration. The analysis described in this article as an example relies on the Apache Mahout library implementation of the k-means clustering algorithm.
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