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Design Pattern Prediction From Source Code Using LLM-Based Feature Engineering and SVM Classification
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DOI：10.1049/sfw2/7163249.png)
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Abstract
En 中文
Typical source code (SC) metrics are useful in identifying and predicting the used design patterns in typical Java and Kotlin projects. However, typical SC metrics-based prediction tends to be less accurate. This research presents a novel idea of detecting various design patterns in a code with the help of large language model (LLM)-based features extraction, instead of using conventional SC metrics typically used in the existing approaches. This research aims to identify and extract using architectural design patterns with the help of various LLM-based feature extraction and supervised machine learning (ML) algorithms. In the proposed approach, LLM-based various 24 design pattern features are extracted instead of the start-of-the-art metrics used for prediction of the design pattern of a particular SC of project. This paper mainly contributes to intelligent and automated software design and development in terms of artificial intelligence (AI)-based detection of design patterns for the purpose of reengineering. In addition to this, this research also aims to investigate the role of design patterns features in automated detection of architectural design patterns and study the association in architectural design patterns and its respective and peculiar features. A Python-based implementation of support vector machine (SVM) algorithm was made. The overall accuracy of SVM-based binary classification was 97.30% that guides the performance of the proposed approach.
Keywords:
design pattern features
design patterns
large language models
SVM
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1.3
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