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Software quality estimation is a challenging and time-consuming activity, andmodels are crucial to face the complexity of such activity on modern softwareapplications. In this context, software refactoring is a crucial activitywithin development life-cycles where requirements and functionalities rapidlyevolve. One main challenge is that the improvement of distinctive qualityattributes may require contrasting refactoring actions on software, as fortrade-off between performance and reliability (or other non-functionalattributes). In such cases, multi-objective optimization can provide thedesigner with a wider view on these trade-offs and, consequently, can lead toidentify suitable refactoring actions that take into account independent oreven competing objectives. In this paper, we present an approach that exploitsNSGA-II as the genetic algorithm to search optimal Pareto frontiers forsoftware refactoring while considering many objectives. We consider performanceand reliability variations of a model alternative with respect to an initialmodel, the amount of performance antipatterns detected on the modelalternative, and the architectural distance, which quantifies the effort toobtain a model alternative from the initial one. We applied our approach on twocase studies: a Train Ticket Booking Service, and CoCoME. We observed that ourapproach is able to improve performance (by up to 42\%) while preserving oreven improving the reliability (by up to 32\%) of generated model alternatives.We also observed that there exists an order of preference of refactoringactions among model alternatives. We can state that performance antipatternsconfirmed their ability to improve performance of a subject model in thecontext of many-objective optimization. In addition, the metric that we adoptedfor the architectural distance seems to be suitable for estimating therefactoring effort.

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