Toward a token-based approach to concern detection in MATLAB sources

Miguel P. Monteiro, Nuno C. Marques, Bruno Silva, Bruno Palma, João Cardoso

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


Matrix and data manipulation programming languages are an essential tool for data analysts. However, these languages are often unstructured and lack modularity mechanisms. This paper presents a business intelligence approach for studying the manifestations of lack of modularity support in that kind of languages. The study is focused on MATLAB as a well established representative of those languages. We present a technique for the automatic detection and quantification of concerns in MATLAB, as well as their exploration in a code base. Ubiquitous Self Organizing Map (UbiSOM) is used based on direct usage of indicators representing different sets of tokens in the code. UbiSOM is quite effective to detect patterns of co-occurrence between multiple concerns. To illustrate, a repository comprising over 35, 000 MATLAB files is analyzed using the technique and relevant conclusions are drawn.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence - 18th EPIA Conference on Artificial Intelligence, EPIA 2017, Proceedings
EditorsZ. Vale, E. Oliveira, J. Gama, H. Lopes Cardoso
Place of PublicationCham
PublisherSpringer Verlag
Number of pages12
Volume10423 LNAI
ISBN (Electronic)978-3-319-65340-2
ISBN (Print)978-331965339-6
Publication statusPublished - 2017
Event18th EPIA Conference on Artificial Intelligence, EPIA 2017 - Porto, Portugal
Duration: 5 Sep 20178 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Volume10423 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th EPIA Conference on Artificial Intelligence, EPIA 2017


  • Business intelligence
  • Concern metrics
  • Concern mining
  • Modularity
  • Self-organizing maps
  • Token-based technique


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