TransDPR: Design Pattern Recognition Using Programming Language Models
Paper i proceeding, 2023

Current Design Pattern Recognition (DPR) methods have limitations, such as the reliance on semantic information, limited recognition of novel or modified pattern versions, and other factors. We present an introductory DPR technique by using a Programming Language Model (PLM) called TransDPR, which utilizes a Facebook pre-trained model (TransCoder), which is a Cross-lingual programming Language Model (XLM) based on a transformer architecture. We leverage an n-dimensional vector representation of programs and apply logistic regression to learn design patterns (DPs). Our approach utilizes the GitHub repository to collect singleton and prototype DP programs written in C++ source code. Our results indicate that TransDPR achieves 90% accuracy and an F1-score of 0.88 on open-source projects. We evaluate the proposed model on two developed modules from Volvo Cars and invite the original developers to validate the prediction results.

Författare

Sushant Kumar Pandey

Software Engineering 1

Göteborgs universitet

Miroslaw Staron

Göteborgs universitet

Chalmers, Data- och informationsteknik, Software Engineering

Jennifer Horkoff

Software Engineering 1

Göteborgs universitet

Miroslaw ochodek

Politechnika Poznanska

Nicholas Mucci

Volvo Cars

Darko Durisic

Volvo Cars

Proceedings of the ACM-IEEE International Symposium on Empirical Software Engineering and Measurement

19493770 (ISSN)


9781665452236 (ISBN)

17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2023
New Orleans, USA,

Ämneskategorier (SSIF 2025)

Programvaruteknik

Datavetenskap (datalogi)

DOI

10.55060/j.jseas.231018.001

Mer information

Senast uppdaterat

2025-06-26