000057744 001__ 57744
000057744 005__ 20190709135419.0
000057744 0247_ $$2doi$$a10.1007/s10515-015-0186-2
000057744 0248_ $$2sideral$$a93774
000057744 037__ $$aART-2017-93774
000057744 041__ $$aeng
000057744 100__ $$0(orcid)0000-0001-7982-0359$$aRodríguez, Ricardo J.$$uUniversidad de Zaragoza
000057744 245__ $$aA Petri Net Tool for Software Performance Estimation Based on Upper Throughput Bounds
000057744 260__ $$c2017
000057744 5060_ $$aAccess copy available to the general public$$fUnrestricted
000057744 5203_ $$aFunctional and non-functional properties analysis (i.e., dependability, security, or performance) ensures that requirements are fulfilled during the design phase of software systems. However, the Unified Modelling Language (UML), standard de facto in industry for software systems modelling, is unsuitable for any kind of analysis but can be tailored for specific analysis purposes through profiling. For instance, the MARTE profile enables to annotate performance data within UML models that can be later transformed to formal models (e.g., Petri nets or Timed Automatas) for performance evaluation. A performance (or throughput) estimation in such models normally relies on a whole exploration of the state space, which becomes unfeasible for large systems. To overcome this issue upper throughput bounds are computed, which provide an approximation to the real system throughput with a good complexity-accuracy trade-off. This paper introduces a tool, named PeabraiN, that estimates the performance of software systems via their UML models. To do so, UML models are transformed to Petri nets where performance is estimated based on upper throughput bounds computation. PeabraiN also allows to compute other features on Petri nets, such as the computation of upper and lower marking place bounds, and to simulate using an approximate (continuous) method. We show the applicability of PeabraiN by evaluating the performance of a building closed circuit TV system.
000057744 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/TIN2014-58457-R$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 644869-DICE$$9info:eu-repo/grantAgreement/EC/H2020/644869/EU/Developing Data-Intensive Cloud Applications with Iterative Quality Enhancements/DICE
000057744 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000057744 590__ $$a1.806$$b2017
000057744 591__ $$aCOMPUTER SCIENCE, SOFTWARE ENGINEERING$$b32 / 104 = 0.308$$c2017$$dQ2$$eT1
000057744 592__ $$a0.317$$b2017
000057744 593__ $$aSoftware$$c2017$$dQ3
000057744 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/submittedVersion
000057744 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000057744 773__ $$g24 (2017), 73-99$$pAutomated Software Engineering$$tAutomated Software Engineering$$x0928-8910
000057744 8564_ $$s2318332$$uhttps://zaguan.unizar.es/record/57744/files/texto_completo.pdf$$yPreprint
000057744 8564_ $$s6567$$uhttps://zaguan.unizar.es/record/57744/files/texto_completo.jpg?subformat=icon$$xicon$$yPreprint
000057744 909CO $$ooai:zaguan.unizar.es:57744$$particulos$$pdriver
000057744 951__ $$a2019-07-09-11:25:03
000057744 980__ $$aARTICLE