Performance of Genetic Algorithms in the Context of Software Model Refactoring

TL;DR

This paper evaluates the performance of three genetic algorithms in software model refactoring, highlighting differences in speed and memory usage to inform better algorithm selection.

Contribution

It provides a comparative analysis of three genetic algorithms' performance and solution quality in the context of software refactoring.

Findings

PESA2 is the fastest algorithm.

NSGA-II uses the least memory.

Significant performance differences exist among the algorithms.

Abstract

Software systems continuously evolve due to new functionalities, requirements, or maintenance activities. In the context of software evolution, software refactoring has gained a strategic relevance. The space of possible software refactoring is usually very large, as it is given by the combinations of different refactoring actions that can produce software system alternatives. Multi-objective algorithms have shown the ability to discover alternatives by pursuing different objectives simultaneously. Performance of such algorithms in the context of software model refactoring is of paramount importance. Therefore, in this paper, we conduct a performance analysis of three genetic algorithms to compare them in terms of performance and quality of solutions. Our results show that there are significant differences in performance among the algorithms (e.g., PESA2 seems to be the fastest one, while NSGA-II shows the least memory usage).