On the Role of Search Budgets in Model-Based Software Refactoring Optimization

TL;DR

This paper investigates how search budgets influence the effectiveness and structural diversity of solutions in model-based software refactoring optimization using evolutionary algorithms.

Contribution

It provides an analysis of the impact of time-limited search budgets on Pareto front quality and solution diversity in software design optimization.

Findings

Search budgets affect Pareto front quality and solution diversity.

Different algorithms behave distinctly under search budget constraints.

Design alternatives under budgets are structurally different from unrestricted solutions.

Abstract

Software model optimization is a process that automatically generates design alternatives aimed at improving quantifiable non-functional properties of software systems, such as performance and reliability. Multi-objective evolutionary algorithms effectively help designers identify trade-offs among the desired non-functional properties. To reduce the use of computational resources, this work examines the impact of implementing a search budget to limit the search for design alternatives. In particular, we analyze how time budgets affect the quality of Pareto fronts by utilizing quality indicators and exploring the structural features of the generated design alternatives. This study identifies distinct behavioral differences among evolutionary algorithms when a search budget is implemented. It further reveals that design alternatives generated under a budget are structurally different from those produced without one. Additionally, we offer recommendations for designers on selecting algorithms in relation to time constraints, thereby facilitating the effective application of automated refactoring to improve non-functional properties.