A metamodel for the specification and verification of model refactoring actions

Arcelli, Davide; Cortellessa, Vittorio; Pompeo, Daniele Di

doi:10.1145/3242163.3242167

“…Each solution that our evolutionary algorithm produces is a sequence of refactoring actions that, once applied to an initial model, leads to a model alternative that shows different non-functional properties. Since our refactoring actions are combined during the evolutionary approach, we exploit the feasibility engine that verifies in advance whether a sequence of refactoring actions is feasible or not [35].…”

Section: The Refactoring Enginementioning

confidence: 99%

Many-Objective Optimization of Non-Functional Attributes based on Refactoring of Software Models

Cortellessa¹,

Pompeo²,

Stoico³

et al. 2023

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“…Each solution that our evolutionary algorithm produces is a sequence of refactoring actions that, once applied to an initial model, leads to a model alternative that shows different non-functional properties. Since our refactoring actions are combined during the evolutionary approach, we exploit the feasibility engine that verifies in advance whether a sequence of refactoring actions is feasible or not [30].…”

Section: B the Refactoring Enginementioning

confidence: 99%

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

Cortellessa¹,

Pompeo²,

Stoico³

et al. 2021

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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.

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