Context: 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. In this context, software refactoring is a crucial activity within development life-cycles where requirements and functionalities rapidly evolve. Objective: One main challenge is that the improvement of distinctive quality attributes may require contrasting refactoring actions on software, as for trade-off between performance and reliability (or other non-functional attributes). 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 refactoring actions that take into account independent or even competing objectives. Method: In this paper, we present an approach that exploits the NSGA-II as the genetic algorithm to search optimal Pareto frontiers for software refactoring while considering many objectives. We consider performance and reliability variations of a model alternative with respect to an initial model, the amount of performance antipatterns detected on the model alternative, and the architectural distance, which quantifies the effort to obtain a model alternative from the initial one. Results: We applied our approach on two case studies: a Train Ticket Booking Service, and CoCoME. We observed that our approach is able to improve performance (by up to 42%) while preserving or even improving the reliability (by up to 32%) of generated model alternatives. We also observed that there exists an order of preference of refactoring actions among model alternatives. Conclusion: Based on our analysis, we can state that performance antipatterns confirmed their ability to improve performance of a subject model in the context of many-objective optimization. In addition, the metric that we adopted for the architectural distance seems to be suitable for estimating the refactoring effort.
Many-objective optimization of non-functional attributes based on refactoring of software models
Cortellessa V.
;Di Pompeo D.;Stoico V.;Tucci M.
2023-01-01
Abstract
Context: 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. In this context, software refactoring is a crucial activity within development life-cycles where requirements and functionalities rapidly evolve. Objective: One main challenge is that the improvement of distinctive quality attributes may require contrasting refactoring actions on software, as for trade-off between performance and reliability (or other non-functional attributes). 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 refactoring actions that take into account independent or even competing objectives. Method: In this paper, we present an approach that exploits the NSGA-II as the genetic algorithm to search optimal Pareto frontiers for software refactoring while considering many objectives. We consider performance and reliability variations of a model alternative with respect to an initial model, the amount of performance antipatterns detected on the model alternative, and the architectural distance, which quantifies the effort to obtain a model alternative from the initial one. Results: We applied our approach on two case studies: a Train Ticket Booking Service, and CoCoME. We observed that our approach is able to improve performance (by up to 42%) while preserving or even improving the reliability (by up to 32%) of generated model alternatives. We also observed that there exists an order of preference of refactoring actions among model alternatives. Conclusion: Based on our analysis, we can state that performance antipatterns confirmed their ability to improve performance of a subject model in the context of many-objective optimization. In addition, the metric that we adopted for the architectural distance seems to be suitable for estimating the refactoring effort.Pubblicazioni consigliate
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