Using Modularity Metrics to assist Move Method Refactoring of Large System

TL;DR

This paper presents an approach that uses modularity metrics and GPU acceleration to automatically suggest move method refactorings in large software systems, improving modularity efficiently.

Contribution

It introduces a method combining modularity metrics with GPU-based computation to identify refactoring opportunities in large systems.

Findings

GPU acceleration significantly reduces metric computation time.

The approach enables continuous assessment of modularity during system evolution.

Refactoring suggestions improve system modularity without negative impacts.

Abstract

For large software systems, refactoring activities can be a challenging task, since for keeping component complexity under control the overall architecture as well as many details of each component have to be considered. Product metrics are therefore often used to quantify several parameters related to the modularity of a software system. This paper devises an approach for automatically suggesting refactoring opportunities on large software systems. We show that by assessing metrics for all components, move methods refactoring an be suggested in such a way to improve modularity of several components at once, without hindering any other. However, computing metrics for large software systems, comprising thousands of classes or more, can be a time consuming task when performed on a single CPU. For this, we propose a solution that computes metrics by resorting to GPU, hence greatly shortening computation time. Thanks to our approach precise knowledge on several properties of the system can be continuously gathered while the system evolves, hence assisting developers to quickly assess several solutions for reducing modularity issues.