Using Modularity Metrics to assist Move Method Refactoring of Large System
This work addresses the problem of managing complexity in large software systems for developers, offering an incremental improvement by automating refactoring suggestions and speeding up metric calculations.
The paper tackles the challenge of refactoring large software systems by proposing an automated approach that uses modularity metrics to suggest move method refactoring opportunities, improving modularity across multiple components without negative side effects, and accelerates metric computation by leveraging GPU processing to reduce time from hours to minutes for systems with thousands of classes.
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.