Comparing Word-based and AST-based Models for Design Pattern Recognition
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.
Fri 8 DecDisplayed time zone: Pacific Time (US & Canada) change
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14:00 30mPaper | The FormAI Dataset: Generative AI in Software Security Through the Lens of Formal Verification PROMISE 2023 Norbert Tihanyi Technology Innovation Institute, Tamas Bisztray University of Oslo, Ridhi Jain Technology Innovation Institute (TII), Abu Dhabi, UAE, Mohamed Amine Ferrag Technology Innovation Institute, Lucas C. Cordeiro The University of Manchester, UK, Vasileios Mavroeidis University of Oslo DOI | ||
14:30 30mPaper | Comparing Word-based and AST-based Models for Design Pattern Recognition PROMISE 2023 Sivajeet Chand Dept. of CSE Chalmers | University of Gothenburg, Sweden, Sushant Kumar Pandey Chalmers and University of Gothenburg, Jennifer Horkoff Chalmers and the University of Gothenburg, Miroslaw Staron University of Gothenburg, Miroslaw Ochodek Poznan University of Technology, Darko Durisic R&D, Volvo Cars, Gothenburg, Sweden DOI | ||
15:00 30mPaper | On Effectiveness of Further Pre-training on BERT models for Story Point Estimation PROMISE 2023 Sousuke Amasaki Okayama Prefectural University DOI |