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Agglomerative algorithm footprint visualisation.

Agglomerative algorithm footprint visualisation.

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Maintenance of existing software requires a large amount of time for comprehending the source code. The architecture of a software, however, may not be clear to maintainers if up to date documentations are not available. Software clustering is often used as a remodularisation and architecture recovery technique to help recover a semantic representa...

Contexts in source publication

Context 1
... the new coordinate system for agglomerative clustering algorithm, we visualise the footprints of the different techniques as shown in Figures 7, 8 and 9. We show the results for the prioritised clustering methods individually (Figures 7(a)-7(c)), by setting the threshold of good performance if the quality of the software clustering is above 70% (MoJoFM). Each data point represents a project, which is labelled as good if the performance of the MoJoFM score is above 70%, and as bad otherwise. ...
Context 2
... Modifiers_max - Figure 8(a) and Manhattan Average 25 (linkage method; distance metric; cluster divisor) - Figure 7(c) ...
Context 3
... Public Methods_mean - • AnonymousClassesQty_mean - Figure 9(c) and Cosine Average 7 - Figure 7(a) ...
Context 4
... the new coordinate system for agglomerative clustering algorithm, we visualise the footprints of the different techniques as shown in Figures 7, 8 and 9. We show the results for the prioritised clustering methods individually (Figures 7(a)-7(c)), by setting the threshold of good performance if the quality of the software clustering is above 70% (MoJoFM). Each data point represents a project, which is labelled as good if the performance of the MoJoFM score is above 70%, and as bad otherwise. ...
Context 5
... Modifiers_max - Figure 8(a) and Manhattan Average 25 (linkage method; distance metric; cluster divisor) - Figure 7(c) ...
Context 6
... Public Methods_mean - • AnonymousClassesQty_mean - Figure 9(c) and Cosine Average 7 - Figure 7(a) ...

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