The Dataset of the Experimental Evaluation of Software Components for Application Design Selection Directed by the Artificial Bee Colony Algorithm

Alexander Gusev, Dmitry Ilin and Evgeny Nikulchev
Additional contact information
Alexander Gusev: Russian Academy of Education, Data-Center, 119121 Moscow, Russia
Dmitry Ilin: MIREA—Russian Technological University, Institute of Integrated Safety, Security and Special Instrumentation, 119454 Moscow, Russia
Evgeny Nikulchev: MIREA—Russian Technological University, Institute of Integrated Safety, Security and Special Instrumentation, 119454 Moscow, Russia

Data, 2020, vol. 5, issue 3, 1-11

Abstract: The paper presents the swarm intelligence approach to the selection of a set of software components based on computational experiments simulating the desired operating conditions of the software system being developed. A mathematical model is constructed, aimed at the effective selection of components from the available alternative options using the artificial bee colony algorithm. The model and process of component selection are introduced and applied to the case of selecting Node.js components for the development of a digital platform. The aim of the development of the platform is to facilitate countrywide simultaneous online psychological surveys in schools in the conditions of unstable internet connection and the large variety of desktop and mobile client devices, running different operating systems and browsers. The module whose development is considered in the paper should provide functionality for the archiving and checksum verification of the survey forms and graphical data. With the swarm intelligence approach proposed in the paper, the effective set of components was identified through a directional search based on fuzzy assessment of the three experimental quality indicators. To simulate the desired operating conditions and to guarantee the reproducibility of the experiments, the virtual infrastructure was configured. The application of swarm intelligence led to reproducible results for component selection after 312 experiments instead of the 1080 experiments needed by the exhaustive search algorithm. The suggested approach can be widely used for the effective selection of software components for distributed systems operating in the given conditions at this stage of their development.

Keywords: swarm intelligence; quality of systems and programs; Node.js; software system development; digital platforms; evolutionary computation; computational experiments (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/5/3/59/pdf (application/pdf)
https://www.mdpi.com/2306-5729/5/3/59/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:5:y:2020:i:3:p:59-:d:381904

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().