On the impact of Performance Antipatterns in multi-objective software model refactoring optimization

TL;DR

This paper presents a multi-objective optimization approach using NSGA-II to improve software refactoring by balancing performance, reliability, and antipatterns, demonstrating that considering antipatterns enhances performance improvements.

Contribution

The paper introduces a novel multi-objective evolutionary algorithm approach that incorporates performance antipatterns into software refactoring optimization, showing improved performance results.

Findings

Incorporating antipatterns improves performance by up to 15%.

The approach effectively balances multiple quality objectives.

Considering antipatterns does not negatively impact other quality attributes.

Abstract

Software quality estimation is a challenging and time-consuming activity, and models are crucial to face the complexity of such activity on modern software applications. One main challenge is that the improvement of distinctive quality attributes may require contrasting refactoring actions on an application, as for trade-off between performance and reliability. In such cases, multi-objective optimization can provide the designer with a wider view on these trade-offs and, consequently, can lead to identify suitable actions that take into account independent or even competing objectives. In this paper, we present an approach that exploits the NSGA-II multi-objective evolutionary algorithm to search optimal Pareto solution frontiers for software refactoring while considering as objectives: i) performance variation, ii) reliability, iii) amount of performance antipatterns, and iv) architectural distance. The algorithm combines randomly generated refactoring actions into solutions (i.e., sequences of actions) and compares them according to the objectives. We have applied our approach on a train ticket booking service case study, and we have focused the analysis on the impact of performance antipatterns on the quality of solutions. Indeed, we observe that the approach finds better solutions when antipatterns enter the multi-objective optimization. In particular, performance antipatterns objective leads to solutions improving the performance by up to 15% with respect to the case where antipatterns are not considered, without affecting the solution quality on other objectives.