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A Surrogate-based Approach for Fast Multi-objective Architectural Refactoring Optimization — arXiv:2609.07389

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.07389
License (as archived): CC0-1.0
Archived: 2026-10-16T14:09:06.992Z
SHA-256 of the archived text: a8ff43abf1b0d3b6ff82b780e0a9468b155f8bdabc33758ed9053eec9fc91178
Stable URL of this page: https://metraipolis.com/documento/dc_c19a612be458

How to cite

A Surrogate-based Approach for Fast Multi-objective Architectural Refactoring Optimization — arXiv:2609.07389 — arXiv (en), https://arxiv.org/abs/2609.07389 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c19a612be458 (Metraipolis archive dc_c19a612be458, sha256 a8ff43abf1b0d3b6ff82b780e0a9468b155f8bdabc33758ed9053eec9fc91178)

BibTeX

@misc{dcc19a612be458,
  title = {A Surrogate-based Approach for Fast Multi-objective Architectural Refactoring Optimization — arXiv:2609.07389},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.07389},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c19a612be458; sha256 a8ff43abf1b0d3b6ff82b780e0a9468b155f8bdabc33758ed9053eec9fc91178},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.07389. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Software model optimization is a process that generates architecture alternatives aimed at improving quantifiable non-functional properties of software systems, such as performance and reliability. Multi-objective evolutionary algorithms are commonly used to explore the search space and help designers identify trade-offs among competing non-functional properties (e.g., through a Pareto front). However, such algorithms face efficiency challenges in complex software models and large design spaces, since evaluating the fitness (i.e., the quality) of each architecture requires analysis tools that become computationally expensive when repeatedly invoked during the search process. In this paper, we explore the construction of surrogate models based on regression techniques to approximate the outputs of these analysis tools at significantly lower computational cost, while maintaining reasonable output accuracy. Our experimental results suggest that surrogate models provide savings of up to $30\%$ in computational time and maintain the Pareto front quality provided by evolutionary algorithms. Also, we observed some differences in the architectural models produced by our approach. Overall, surrogate models constitute a promising approach for scaling multi-objective architecture optimization to larger spaces and complex architectural models.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-09 in cs.SE).
Categorie arXiv: Software Engineering (cs.SE).
Pagina del lavoro: https://arxiv.org/abs/2609.07389 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T13:58:36.390Z dalla pagina https://arxiv.org/catchup/cs.SE/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).