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Auflistung nach Autor:in "Tribastone, Mirco"

1 - 3 von 3
  • Konferenzbeitrag
    Family-based performance analysis of variant-rich software systems
    (Software-engineering and management 2015, 2015) Kowal, Matthias; Schaefer, Ina; Tribastone, Mirco
    The analysis is meant for behavioral models of workflow-type software systems such as data centers or automation systems. We model these systems as a UML activity diagram with performance annotations to compute a performance prediction. A product-based (PB) analysis is not always viable, since we can have several variants, and each variant has its own performance model, which is why we cannot reuse the results of a single variant. Hence, we propose a family-based (FB) performance analysis relying on symbolic computation. The FB analysis can be significantly faster than PB analysis, especially for large-scale workflow model, thus enabling efficient calculation of large parameter spaces.
  • Zeitschriftenartikel
    Scalable Performance Evaluation of Computer Systems
    (Softwaretechnik-Trends Band 34, Heft 2, 2011) Tribastone, Mirco
    The present paper provides an overview of recent and ongoing research conducted at the Chair of Programming and Software Engineering of LMU Munich on performance evaluation of large-scale computer systems.
  • Konferenzbeitrag
    Scaling size and parameter spaces in variability-aware software performance models
    (Software Engineering 2016, 2016) Kowal, Matthias; Tschaikowski, Max; Tribastone, Mirco; Schaefer, Ina
    Model-based software performance engineering often requires the analysis of many instances of a model to find optimizations or to do capacity planning. These performance predictions get increasingly more difficult with larger models due to state space explosion as well as large parameter spaces since each configuration has its own performance model and must be analyzed in isolation (product-based (PB) analysis). We propose an efficient family-based (FB) analysis using UML activity diagrams with performance annotations. The FB analysis enables us to analyze all configurations at once using symbolic computation. Previous work has already shown that a FB analysis is significant faster than its PB counterpart. This work is an extension of our previous research lifting several limitations.