# The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software Refactoring

arXiv: https://arxiv.org/abs/2001.03338 · HTML: https://mlsanity.com/paper/2001.03338/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2020.

## Built on by (most cited first, 9 of 9)

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- [A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges](https://mlsanity.com/paper/2306.16171/) (2023)
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- [Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection](https://mlsanity.com/paper/2206.08726/) (2022)
- [Learning to predict test effectiveness](https://mlsanity.com/paper/2208.09623/) (2022)
- [Can You Improve My Code? Optimizing Programs with Local Search](https://mlsanity.com/paper/2307.05603/) (2023)
- [On ML-Based Program Translation: Perils and Promises](https://mlsanity.com/paper/2302.10812/) (2023)
- [A Systematic Literature Review on the Use of Machine Learning in Software Engineering](https://mlsanity.com/paper/2406.13877/) (2024)

## Abstract (arXiv)

Refactoring is the process of changing the internal structure of software to improve its quality without modifying its external behavior. Empirical studies have repeatedly shown that refactoring has a positive impact on the understandability and maintainability of software systems. However, before carrying out refactoring activities, developers need to identify refactoring opportunities. Currently, refactoring opportunity identification heavily relies on developers' expertise and intuition. In this paper, we investigate the effectiveness of machine learning algorithms in predicting software refactorings. More specifically, we train six different machine learning algorithms (i.e., Logistic Regression, Naive Bayes, Support Vector Machine, Decision Trees, Random Forest, and Neural Network) with a dataset comprising over two million refactorings from 11,149 real-world projects from the Apache, F-Droid, and GitHub ecosystems. The resulting models predict 20 different refactorings at class, method, and variable-levels with an accuracy often higher than 90%. Our results show that (i) Random Forests are the best models for predicting software refactoring, (ii) process and ownership metrics seem to play a crucial role in the creation of better models, and (iii) models generalize well in different contexts.