Detecting Java software similarities by using different clustering techniques

Capiluppi, A., Di Ruscio, D., Di Rocco, J., Nguyen, P.T. and Ajienka, N. ORCID: 0000-0002-8792-282X, 2020. Detecting Java software similarities by using different clustering techniques. Information and Software Technology, 122: 106279. ISSN 0950-5849


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Background: Research on empirical software engineering has increasingly been conducted by analysing and measuring vast amounts of software systems. Hundreds, thousands and even millions of systems have been (and are) considered by researchers, and often within the same study, in order to test theories, demonstrate approaches or run prediction models. A much less investigated aspect is whether the collected metrics might be context-specific, or whether systems should be better analysed in clusters.

Objective: The objectives of this study are (i) to define a set of clustering techniques that might be used to group similar software systems, and (ii) to evaluate whether a suite of well-known object-oriented metrics is context-specific, and its values differ along the defined clusters.

Method: We group software systems based on three different clustering techniques, and we collect the values of the metrics suite in each cluster. We then test whether clusters are statistically different between each other, using the Kolgomorov-Smirnov (KS) hypothesis testing.

Results: Our results show that, for two of the used techniques, the KS null hypothesis (e.g., the clusters come from the same population) is rejected for most of the metrics chosen: the clusters that we extracted, based on application domains, show statistically different structural properties.

Conclusions: The implications for researchers can be profound: metrics and their interpretation might be more sensitive to context than acknowledged so far, and application domains represent a promising filter to cluster similar systems.

Item Type: Journal article
Publication Title: Information and Software Technology
Creators: Capiluppi, A., Di Ruscio, D., Di Rocco, J., Nguyen, P.T. and Ajienka, N.
Publisher: Elsevier
Date: June 2020
Volume: 122
ISSN: 0950-5849
S095058492030029XPublisher Item Identifier
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 17 Feb 2020 15:16
Last Modified: 31 May 2021 15:06

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