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498 results on '"Vittorio Cortellessa"'

25. Handling non-functional requirements in Model-Driven Development: An ongoing industrial survey.

Author

David Ameller, Xavier Franch, Cristina Gómez 0001, João Araújo 0001, Richard Berntsson-Svensson, Stefan Biffl, Jordi Cabot, Vittorio Cortellessa, Maya Daneva, Daniel Méndez Fernández, Ana Moreira 0001, Henry Muccini, Antonio Vallecillo, Manuel Wimmer, Vasco Amaral 0001, Hugo Bruneliere, Loli Burgueño, Miguel Goulão, Bernhard Schätz, and Sabine Teufl

Published
2015
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29. Architectural Support for Software Performance in Continuous Software Engineering: A Systematic Mapping Study

Author

Romina Eramo, Michele Tucci, Daniele Di Pompeo, Vittorio Cortellessa, Antinisca Di Marco, and Davide Taibi

Subjects
Software Engineering (cs.SE), FOS: Computer and information sciences, History, Computer Science - Software Engineering, Polymers and Plastics, Business and International Management, Industrial and Manufacturing Engineering
Abstract

The continuous software engineering paradigm is gaining popularity in modern development practices, where the interleaving of design and runtime activities is induced by the continuous evolution of software systems. In this context, performance assessment is not easy, but recent studies have shown that architectural models evolving with the software can support this goal. In this paper, we present a mapping study aimed at classifying existing scientific contributions that deal with the architectural support for performance-targeted continuous software engineering. We have applied the systematic mapping methodology to an initial set of 215 potentially relevant papers and selected 66 primary studies that we have analyzed to characterize and classify the current state of research. This classification helps to focus on the main aspects that are being considered in this domain and, mostly, on the emerging findings and implications for future research

Published
2023

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

Author

Vittorio Cortellessa, Daniele Di Pompeo, Vincenzo Stoico, and Michele Tucci

Subjects
FOS: Computer and information sciences, Refactoring, Performance, Software architecture, Model-driven engineering, Multi-objective optimization, Reliability, Search-based software engineering, Computer Science Applications, Software Engineering (cs.SE), Computer Science - Software Engineering, Software, Information Systems
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. In this context, software refactoring is a crucial activity within development life-cycles where requirements and functionalities rapidly evolve. 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. In this paper, we present an approach that exploits 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. 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. 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., Comment: Accepted for publication in Information and Software Technologies. arXiv admin note: substantial text overlap with arXiv:2107.06127

Published
2023
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