Publications

@article{Bernardi:EmSE:2018,

title = {A systematic approach for performance assessment using process mining: An industrial experience report},

author = {Simona Bernardi and Juan L. Dom\'{i}nguez and Abel G\'{o}mez and Christophe Joubert and Jos\'{e} Merseguer and Diego Perez-Palacin and Jos\'{e} I. Requeno and Alberto Romeu},

url = {http://rdcu.be/Jz3J},

doi = {10.1007/s10664-018-9606-9},

issn = {1573-7616},

year  = {2018},

date = {2018-03-21},

journal = {Empirical Software Engineering},

volume = {23},

number = {6},

pages = {3394--3441},

abstract = {Software performance engineering is a mature field that offers methods to assess system performance. Process mining is a promising research field applied to gain insight on system processes. The interplay of these two fields opens promising applications in the industry. In this work, we report our experience applying a methodology, based on process mining techniques, for the performance assessment of a commercial data-intensive software application. The methodology has successfully assessed the scalability of future versions of this system. Moreover, it has identified bottlenecks components and replication needs for fulfilling business rules. The system, an integrated port operations management system, has been developed by Prodevelop, a medium-sized software enterprise with high expertise in geospatial technologies. The performance assessment has been carried out by a team composed by practitioners and researchers. Finally, the paper offers a deep discussion on the lessons learned during the experience, that will be useful for practitioners to adopt the methodology and for researcher to find new routes.},

keywords = {DICE, Experience Report, Modeling and Analysis of Real Time and Embedded systems (MARTE), Petri net (PN), Process Mining, Simulation, Software Perfomance, Unified Modeling Language (UML)},

pubstate = {published},

tppubtype = {article}

}

@conference{Gomez:JISBD:2016,

title = {Una herramienta para evaluar el rendimiento de aplicaciones intensivas en datos},

author = {Abel G\'{o}mez and Jos\'{e} Merseguer},

editor = {Jes\'{u}s Garc\'{i}a Molina},

url = {http://hdl.handle.net/11705/JISBD/2016/026},

year  = {2016},

date = {2016-09-13},

booktitle = {Actas de las XXI Jornadas de Ingenier\'{i}a del Software y Bases de Datos (JISBD 2016)},

publisher = {SISTEDES},

address = {Salamanca, Spain},

abstract = {Las aplicaciones intensivas en datos (AID) que usan tecnolog\'{i}as de Big Data se est\'{a}n convirtiendo en una parte importante del mercado de desarrollo de software. Sin embargo, las t\'{e}cnicas --y su automatizaci\'{o}n-- para el asesoramiento de la calidad para este tipo de aplicaciones es claramente insuficiente. El proyecto DICE H2020 tiene como objetivo definir metodolog\'{i}as y crear herramientas para desarrollar y monitorizar AID mediante t\'{e}cnicas de ingenier\'{i}a dirigida por modelos. En este art\'{i}culo presentamos un componente clave del proyecto DICE: su herramienta de simulaci\'{o}n. Esta herramienta es capaz de evaluar el rendimiento de AID simulando su comportamiento mediante modelos de redes de Petri. Como complemento, existe a disposici\'{o}n un v\'{i}deo mostrando la herramienta en http://tiny.cc/z1qzay.},

keywords = {Computer Aided Design (CASE), Data-Intensive Applications (DIA), DICE, Model-Driven Engineering (MDE), Modeling and Analysis of Real Time and Embedded systems (MARTE), Petri net (PN), Simulation, UML Profiles, Unified Modeling Language (UML)},

pubstate = {published},

tppubtype = {conference}

}