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dc.contributor.authorAndreopoulos, Bill
dc.contributor.authorAn, Aijun
dc.contributor.authorTzerpos, Vassilios
dc.contributor.authorWang, Xiaogang
dc.date.accessioned2007-03-27T18:47:46Z
dc.date.available2007-03-27T18:47:46Z
dc.date.issued2006
dc.identifier.citationAndreopoulos, B., An, A., Tzerpos, V., & Wang, X. (2007). Clustering large software systems at multiple layers. Information and Software Technology, 49(3), 244–254. https://doi.org/10.1016/j.infsof.2006.10.010
dc.identifier.issn0950-5849
dc.identifier.urihttp://hdl.handle.net/10315/924
dc.identifier.urihttps://doi.org/10.1016/j.infsof.2006.10.010
dc.description.abstractSoftware clustering algorithms presented in the literature rarely incorporate in the clustering process dynamic information, such as the number of function invocations during run time. Moreover, the structure of a software system is often multi-layered, while existing clustering algorithms often create flat system decompositions. This paper presents a software clustering algorithm called MULICsoft that incorporates in the clustering process both static and dynamic information. MULICsoft produces layered clusters with the core elements of each cluster assigned to the top layer. We present experimental results of applying MULICsoft to a large open-source system. Comparison with existing software clustering algorithms indicates that MULICsoft is able to produce decompositions that are close to those created by system experts.
dc.language.isoen
dc.publisherInformation and Software Technology
dc.rights© 2006. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectSoftware clustering
dc.subjectMultiple layer
dc.subjectCategorical
dc.subjectMULIC
dc.titleClustering Large Software Systems at Multiple Layers
dc.typeArticle


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© 2006. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
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