Toward a Token-Based Approach to Concern Detection in MATLAB Sources

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Monteiro, MP (Autor) Outra; Marques, NC (Autor) Outra; Silva, B (Autor) Outra; Palma, B (Autor) Outra; João M. P. Cardoso (Autor) FEUP

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2017)

Páginas: 573-584

18th EPIA Conference on Artificial Intelligence (EPIA)

Univ Porto, Fac Engn, Porto, PORTUGAL, SEP 05-08, 2017

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00M-YHK

DOI: 10.1007/978-3-319-65340-2_47

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12