Fuzzy clustering of software metrics
نویسندگان
چکیده
We investigate the use of fuzzy clustering for the analysis of software metrics databases. Software metrics are collected at various points during software development, in order to monitor and control the quality of a software product. We use fuzzy clustering to examine three collections of software metrics. This is one of the very few attempts to use unsupervised learning in the software metrics domain, even though unsupervised learning seems more appropriate for this application domain. Some characteristics of this application domain that have significant implications for machine learning are highlighted and discussed. Our results illustrate how unsupervised learning can he used in software quality control.
منابع مشابه
Software Quality Assessment Using a Fuzzy Clustering Approach
Metrics have long been studied as a way to assess the quality and complexity of software, and recently this has been applied to objectoriented software as well. However one of their shortcomings is the lack of relevant result interpretation. Related to this, there is an aspect that has a decisive influence on the accuracy of the results obtained: the issue of software metrics threshold values. ...
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