Створення методу забезпечення якості коментарів у системах контролю версій на основі моделей Word2Vec, FastText та GloVe

• Date: 2026
• Main Authors: Semonov, Bohdan, Pogorilyy, Sergiy
• Format: Article
• Language: English
• Published: The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2026
• Subjects: AdamW-алгоритм; commit message; GitHub REST API; GRU-шар; MLP; RNN; вихідний текст програми; ПЗ; повідомлення про внесені зміни; репозиторій; середнє гармонійне; система контролю версій
• Online Access: https://journal.iasa.kpi.ua/article/view/365243
• Journal Title: System research and information technologies

This paper substantiates the relevance of addressing the problem of ensuring the quality of change descriptions in source code files within version control systems. To filter commit messages, machine learning methods are employed, including neural networks of various architectures. The use of neural networks is justified by the need to identify descriptions that accurately reflect the intent of the changes. A comparative analysis of word embedding methods (Word2Vec, FastText, and GloVe) was conducted, along with their application in binary classifiers such as MLP and RNN for filtering code changes. The models were trained on a dataset of change descriptions collected via the GitHub REST API. Model performance was evaluated using Accuracy and F1-score metrics. The effectiveness of the Google Colab environment for prototyping machine learning models was also confirmed.