Extended abstract of E-SC4R: Explaining software clustering for remodularisation

Alvin Jian Jia Tan, Chun Yong Chong, Aldeida Aleti

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

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.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2023
EditorsTao Zhang, Xin Xia, Nicole Novielli
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages846-847
Number of pages2
ISBN (Electronic)9781665452786
ISBN (Print)9781665452793
DOIs
Publication statusPublished - 2023
EventIEEE International Conference on Software Analysis, Evolution, and Reengineering 2023 - Macao, China
Duration: 21 Mar 202324 Mar 2023
Conference number: 30th
https://ieeexplore.ieee.org/xpl/conhome/10123438/proceeding (Proceedings)
https://saner2023.must.edu.mo/ (Website)

Publication series

NameProceedings - 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2023
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)1534-5351
ISSN (Electronic)2640-7574

Conference

ConferenceIEEE International Conference on Software Analysis, Evolution, and Reengineering 2023
Abbreviated titleSANER 2023
Country/TerritoryChina
CityMacao
Period21/03/2324/03/23
Internet address

Keywords

  • architecture recovery
  • feature extraction
  • footprint visualisation
  • software clustering
  • software remodularisation

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