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.
Highlights
In previous decades, a significant amount of research was devoted to the development of an optimal modular architecture for component-oriented programming [1] based on a set of criteria [2] including the quality assessment of modular software architecture [3], increasing the productivity of modular software by various methods, including clustering methods [4], genetic algorithms [5], and other evolutionary algorithms [6]
It is necessary to guarantee the reproducibility of the experiments and the conformity of the experimental environment to the real operating conditions of the software system, which can be done through using the preconfigured virtual infrastructure, simulating the desired operating conditions [8]
To reduce the number of experiments the developers can use the swarm intelligence approach presented in the paper
Summary
A significant amount of research was devoted to the development of an optimal modular architecture for component-oriented programming [1] based on a set of criteria [2] including the quality assessment of modular software architecture [3], increasing the productivity of modular software by various methods, including clustering methods [4], genetic algorithms [5], and other evolutionary algorithms [6]. These studies were aimed at the a priori optimization of component-oriented software. Swarm intelligence can be applied to control the process of selection as it becomes possible to make sure that the best results are reproduced while the mediocre stacks are quickly removed from consideration by the swarm
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