Optimizing Python Code Metrics Feature Reduction Through Meta-Analysis and Swarm Intelligence

Marina Ivanova, Zamira Kholmatova, Nikolay Pavlenko

2025

Abstract

Feature selection plays an important role in reducing the complexity of datasets while preserving the integrity of data for analysis and predictive tasks. This study tackles this problem in the context of optimizing metrics for Python source code quality assessment. We propose a combination of meta-analysis with the Modified Discrete Artificial Bee Colony (MDisABC) algorithm to identify an optimal subset of metrics for evaluating code repositories. A systematic preprocessing step using correlation-based thresholds (0.7, 0.8, 0.9) through random-effects meta-analysis effectively reduces redundancy while retaining relevant metrics. The MDisABC algorithm is then employed to minimize Sammon error, ensuring the preservation of structural properties in the reduced feature space. Our results demonstrate significant error reductions, faster convergence, and consistent identification of key metrics that are critical for assessing code quality. This work highlights the utility of integrating meta-analysis and nature-inspired algorithms for feature selection and establishes a foundation for scalable, accurate, and interpretable models in software quality assessment. Future research could expand this methodology to other programming languages and explore alternative algorithms or cost functions for more comprehensive evaluations. All the relevant code can be found on our GitHub repository∗.

Paper Citation

in Harvard Style

Ivanova M., Kholmatova Z. and Pavlenko N. (2025). Optimizing Python Code Metrics Feature Reduction Through Meta-Analysis and Swarm Intelligence. In Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering - Volume 1: MODELSWARD; ISBN 978-989-758-729-0, SciTePress, pages 338-345. DOI: 10.5220/0013377200003896

in Bibtex Style

@conference{modelsward25,
author={Marina Ivanova and Zamira Kholmatova and Nikolay Pavlenko},
title={Optimizing Python Code Metrics Feature Reduction Through Meta-Analysis and Swarm Intelligence},
booktitle={Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering - Volume 1: MODELSWARD},
year={2025},
pages={338-345},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013377200003896},
isbn={978-989-758-729-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Model-Based Software and Systems Engineering - Volume 1: MODELSWARD
TI - Optimizing Python Code Metrics Feature Reduction Through Meta-Analysis and Swarm Intelligence
SN - 978-989-758-729-0
AU - Ivanova M.
AU - Kholmatova Z.
AU - Pavlenko N.
PY - 2025
SP - 338
EP - 345
DO - 10.5220/0013377200003896
PB - SciTePress