E-SC4R: Explaining Software Clustering for Remodularisation✩

Alvin Jian Jia Tan

doi:10.1016/j.jss.2021.111162

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E-SC4R: Explaining Software Clustering for Remodularisation✩

2022-04-01

Alvin Jian Jia Tan

Chun Yong Chong *

Aldeida Aleti

DOI：10.1016/j.jss.2021.111162

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Abstract

En 中文

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. Due to the diverse domains, structure, and behaviour of software systems, the suitability of different clustering algorithms 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 could only represent a narrow perspective of the whole picture, researchers might 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 most suitable algorithm and configuration that can achieve the best MoJoFM value from our existing pool of choices for a particular software system. The E-SC4R framework is tested on 30 open-source software systems with varying sizes and domains, and demonstrates that it can 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 generated through the application of dimensionality reduction techniques. (c) 2021 Elsevier Inc. All rights reserved.

Keywords:

Architecture recovery

Software remodularisation

Software clustering

Feature extraction

Footprint visualisation

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