Guidelines for Artifacts to Support Industry-Relevant Research on Self-Adaptation
Artikel i vetenskaplig tidskrift, 2022
Artifacts support evaluating new research results and help comparing them with the state of the art in a field of interest. Over the past years, several artifacts have been introduced to support research in the field of self-adaptive systems. While these artifacts have shown their value, it is not clear to what extent these artifacts support research on problems in self-adaptation that are relevant to industry. This paper provides a set of guidelines for artifacts that aim at supporting industry-relevant research on self- adaptation. The guidelines that are grounded on data obtained from a survey with practitioners were derived during working sessions at the 17th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. Artifact providers can use the guidelines for aligning future artifacts with industry needs; they can also be used to evaluate the industrial relevance of existing artifacts. We also propose an artifact template.
Författare
Danny Weyns
KU Leuven
Ilias Gerostathopoulos
Vrije Universiteit Amsterdam
Barbora Buhnova
Masarykova Univerzita
Nicolás Cardozo
Universidad de los Andes
Emilia Cioroaica
Fraunhofer Institute for Energy Economics and Energy System Technology IEE
Ivana Dusparic
Trinity College Dublin
Lars Grunske
Humboldt-Universität zu Berlin
Pooyan Jamshidi
University of South Carolina
Christine Julien
The University of Texas at Austin
Judith Michael
RWTH Aachen University
Gabriel Moreno
Carnegie Mellon University (CMU)
Shiva Nejati
University of Ottawa
Patrizio Pelliccione
Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering
Federico Quin
KU Leuven
Genaina Nunes Rodrigues
Universidade de Brasilia
Bradley Schmerl
Carnegie Mellon University (CMU)
Marco Vieira
Universidade de Coimbra
Thomas Vogel
Hasso-Plattner-Institut fur Softwaresystemtechnik GmbH
Rebekka Wohlrab
Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering
ACM SIGSOFT Software Engineering Notes
0163-5948 (ISSN)
Vol. 47 4 18-24Ämneskategorier
Programvaruteknik
Datavetenskap (datalogi)
DOI
10.1145/3561846.3561852