A fuzzy genetic automatic refactoring approach to improve software maintainability and flexibility

Abstract

The creation of high-quality software is of great importance in the current state of the enterprise systems. High-quality software should contain certain features including flexibility, maintainability, and a well-designed structure. Correctly adhering to the object-oriented principles is a primary approach to make the code more flexible. Developers usually try to leverage these principles, but many times neglecting them due to the lack of time and the extra costs involved. Therefore, sometimes they create confusing, complex, and problematic structures in code known as code smells. Code smells have specific and well-known anti-patterns that can be corrected after their identification with the help of the refactoring techniques. This process can be performed either manually by the developers or automatically. In this paper, an automated method for identifying and refactoring a series of code smells in the Java programs is introduced. The primary mechanism used for performing such automated refactoring is by leveraging a fuzzy genetic method. Besides, a graph model is used as the core representation scheme along with the corresponding measures such as betweenness, load, in-degree, out-degree, and closeness centrality, to identify the code smells in the programs. Then, the applied fuzzy approach is combined with the genetic algorithm to refactor the code using the graph-related features. The proposed method is evaluated using the Freemind, Jag, JGraph, and JUnit as sample projects and compared the results against the Fontana dataset which contains results from IPlasma, FluidTool, Anti-patternScanner, PMD, and Maeinescu. It is shown that the proposed approach can identify on average 68.92% of the bad classes similar to the Fontana dataset and also refactor 77% of the classes correctly with respect to the coupling measures. This is a noteworthy result among the currently existing refactoring mechanisms and also among the studies that consider both the identification and the refactoring of the bad smells.