A Meta-Learning Approach for Software Refactoring

TL;DR

This paper introduces a meta-learning approach using MAML to detect software refactoring opportunities efficiently, especially in low-data scenarios, achieving high accuracy with minimal annotated data.

Contribution

It formulates refactoring opportunity detection as a few-shot classification problem and applies MAML to improve performance in low-resource settings.

Findings

Achieved 91% accuracy in detecting refactoring opportunities.

Demonstrated effectiveness of meta-learning in software engineering tasks.

Reduced data requirements for accurate refactoring detection.

Abstract

Software refactoring is the process of changing the structure of software without any alteration in its behavior and functionality. Presuming it is carried out in appropriate opportunities, refactoring enhances software quality characteristics such as maintainability and extensibility. Thus far, various studies have addressed the problem of detecting proper opportunities for refactoring. Most of them are based on human expertise and are prone to error and non-meticulous. Fortunately, in recent efforts, machine learning methods have produced outstanding results in finding appropriate opportunities for refactoring. Sad to say, Machine learning methods mostly need plenty of data and, consequently, long processing time. Furthermore, there needs to be more annotated data for many types of refactoring, and data collection is time-consuming and costly. Accordingly, in this paper, we have formulated the problem of detecting appropriate opportunities for refactoring as a few-shot classification problem. We have utilized model-agnostic meta-learning (MAML), a recognized meta-learning algorithm, to learn a neural network on tasks from high-resource data. The trained model, then, is adapted to a model with high accuracy for tasks from low-resource data. Experimental results revealed 91% accuracy, which illustrates the effectiveness and competitiveness of our proposed meta-learning model.