This paper proposes a novel method for selecting software design patterns using text classification techniques. Traditionally, this process relies heavily on human expertise, which can be time-consuming and subjective. However, the proposed approach automates pattern identification and recommendation through natural language processing and machine learning, aiming to improve accuracy and scalability. Key components include data preprocessing, feature extraction, and machine learning algorithms. The study seeks to validate the effectiveness of the approach through quantitative analysis. Ultimately, the research aims to enhance the development of resilient and maintainable software systems amidst evolving technological landscapes.

Асаад Ж. (науч. рук. Авксентьева Е.Ю.) SOFTWARE DESIGN PATTERNS SELECTION USING TEXT CLASSIFICATION APPROACH // Сборник тезисов докладов конгресса молодых ученых. Электронное издание. – СПб: Университет ИТМО, [2024]. URL: https://kmu.itmo.ru/digests/article/13533