Trends and Challenges in Machine Learning for Code Summarization and Comprehension: A Systematic Literature Review

Panagiotis Mantos, Fotios Kokkoras, George Kakarontzas

2025

Abstract

This systematic literature review explores current trends in automatic source code summarization and comprehension. Through extraction and analysis of information from six reputable digital libraries, we answered the following three questions: a) Which are the current machine learning models to generate summaries for source code? b) What factors should be considered when selecting an appropriate machine learning model for code summarization and comprehension? c) What are the possible future directions for research and development in machine learning for code summarization and comprehension, considering current limitations and emerging trends. The findings show significant progress with deep learning methods dominating this area.

Paper Citation

in Harvard Style

Mantos P., Kokkoras F. and Kakarontzas G. (2025). Trends and Challenges in Machine Learning for Code Summarization and Comprehension: A Systematic Literature Review. In Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE; ISBN 978-989-758-742-9, SciTePress, pages 468-475. DOI: 10.5220/0013275300003928

in Bibtex Style

@conference{enase25,
author={Panagiotis Mantos and Fotios Kokkoras and George Kakarontzas},
title={Trends and Challenges in Machine Learning for Code Summarization and Comprehension: A Systematic Literature Review},
booktitle={Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE},
year={2025},
pages={468-475},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013275300003928},
isbn={978-989-758-742-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE
TI - Trends and Challenges in Machine Learning for Code Summarization and Comprehension: A Systematic Literature Review
SN - 978-989-758-742-9
AU - Mantos P.
AU - Kokkoras F.
AU - Kakarontzas G.
PY - 2025
SP - 468
EP - 475
DO - 10.5220/0013275300003928
PB - SciTePress