Extended Abstract of E-SC4R: Explaining Software Clustering for Remodularisation

Abstract

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 representation of the software design. However, due to the diverse domain and structure of software systems, the suitability of different clustering techniques for different software systems are not investigated thoroughly. Research that introduce new clustering techniques usually validate their approaches on a specific domain, which might limit its generalisability. If the chosen test subjects only represent a narrow perspective of the whole picture, researchers risk not being able to address the external validity of their findings. This work aims to fill this gap by introducing a new approach, Explaining Software Clustering for Remodularisation (E-SC4R), to evaluate the effectiveness of different software clustering approaches. This work focuses on hierarchical clustering and Bunch clustering algorithms and provides information about their suitability according to the features of the software, which, as a consequence, enables the selection of the optimum technique for a particular software system. The E-SC4R framework is able to characterise both the strengths and weaknesses of the analysed software clustering algorithms using software features extracted from the code. The proposed approach also provides a better understanding of the algorithms’ behaviour by showing a 2D representation of the effectiveness of clustering techniques on the feature space through the application of dimensionality reduction techniques.