Title
Comparing Word-Based and AST-Based Models for Design Pattern Recognition
Authors
[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee
Scientific discipline (Law 2.0)
Year of publication
2023
Chapter type
chapter in monograph / paper
Publication language
english
Keywords
EN
- Programming Language Models
- Design Patterns
- NLP
Abstract
EN Design patterns (DPs) provide reusable and general solutions for frequently encountered problems. Patterns are important to maintain the structure and quality of software products, in particular in large and distributed systems like automotive software. Modern language models (like Code2Vec or Word2Vec) indicate a deep understanding of programs, which has been shown to help in such tasks as program repair or program comprehension, and therefore show promise for DPR in industrial contexts. The models are trained in a self-supervised manner, using a large unlabelled code base, which allows them to quantify such abstract concepts as programming styles, coding guidelines, and, to some extent, the semantics of programs. This study demonstrates how two language models—Code2Vec and Word2Vec, trained on two public automotive repositories, can show the separation of programs containing specific DPs. The results show that the Code2Vec and Word2Vec produce average F1-scores of 0.781 and 0.690 on open-source Java programs, showing promise for DPR in practice.
Date of online publication
08.12.2023
Pages (from - to)
44 - 48
License type
copyright
Open Access Mode
publisher's website
Open Access Text Version
final published version
Date of Open Access to the publication
at the time of publication
Ministry points / chapter
20
Ministry points / conference (CORE)
200
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