GEML: A grammar-based evolutionary machine learning approach for design-pattern detection
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gp-bibliography.bib Revision:1.8010
- @Article{BARBUDO:2021:JSS,
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author = "Rafael Barbudo and Aurora Ramirez and
Francisco Servant and Jose Raul Romero",
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title = "{GEML:} A grammar-based evolutionary machine learning
approach for design-pattern detection",
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journal = "Journal of Systems and Software",
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year = "2021",
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volume = "175",
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pages = "110919",
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month = may,
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keywords = "genetic algorithms, genetic programming, Design
pattern detection, Reverse engineering, Machine
learning, Associative classification, Grammar-guided
genetic programming",
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ISSN = "0164-1212",
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URL = "https://www.sciencedirect.com/science/article/pii/S0164121221000169",
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DOI = "doi:10.1016/j.jss.2021.110919",
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abstract = "Design patterns (DPs) are recognised as a good
practice in software development. However, the lack of
appropriate documentation often hampers traceability,
and their benefits are blurred among thousands of lines
of code. Automatic methods for DP detection have become
relevant but are usually based on the rigid analysis of
either software metrics or specific properties of the
source code. We propose GEML, a novel detection
approach based on evolutionary machine learning using
software properties of diverse nature. Firstly, GEML
makes use of an evolutionary algorithm to extract those
characteristics that better describe the DP, formulated
in terms of human-readable rules, whose syntax is
conformant with a context-free grammar. Secondly, a
rule-based classifier is built to predict whether new
code contains a hidden DP implementation. GEML has been
validated over five DPs taken from a public repository
recurrently adopted by machine learning studies. Then,
we increase this number up to 15 diverse DPs, showing
its effectiveness and robustness in terms of detection
capability. An initial parameter study served to tune a
parameter setup whose performance guarantees the
general applicability of this approach without the need
to adjust complex parameters to a specific pattern.
Finally, a demonstration tool is also provided",
- }
Genetic Programming entries for
Rafael Barbudo Lunar
Aurora Ramirez Quesada
Francisco Javier Servant Cortes
Jose Raul Romero Salguero
Citations