Technology Stack Selection Model for Software Design of Digital Platforms

The article is dedicated to the development of a mathematical model and methodology for evaluating the effectiveness of integrating information technology solutions into digital platforms using virtual simulation infrastructures. The task of selecting a stack of technologies is formulated as the task of selecting elements from sets of possible solutions. This allows us to develop a mathematically unified approach to evaluating the effectiveness of different solutions, such as choosing programming languages, choosing Database Management System (DBMS), choosing operating systems and data technologies, and choosing the frameworks used. Introduced technology compatibility operation and decomposition of the evaluation of the efficiency of the technology stack at the stages of the life cycle of the digital platform development allowed us to reduce the computational complexity of the formation of the technology stack. A methodology based on performance assessments for experimental research in a virtual software-configurable simulation environment has been proposed. The developed solution allows the evaluation of the performance of the digital platform before its final implementation, while reducing the cost of conducting an experiment to assess the characteristics of the digital platform. It is proposed to compare the characteristics of digital platform efficiency based on the use of fuzzy logic, providing the software developer with an intuitive tool to support decision-making on the inclusion of the solution in the technology stack.

[1]  Jürgen Münch,et al.  Raising the odds of success: the current state of experimentation in product development , 2016, Inf. Softw. Technol..

[2]  Seyed Mohammad Hossein Hasheminejad,et al.  Software component identification and selection: A research review , 2019, Softw. Pract. Exp..

[3]  Evgeny Nikulchev,et al.  The Dataset of the Experimental Evaluation of Software Components for Application Design Selection Directed by the Artificial Bee Colony Algorithm , 2020, Data.

[4]  Bo Yang,et al.  An enhanced multi-objective grey wolf optimizer for service composition in cloud manufacturing , 2020, Appl. Soft Comput..

[5]  Vahid Garousi,et al.  Aligning software engineering education with industrial needs: A meta-analysis , 2019, J. Syst. Softw..

[6]  Foutse Khomh,et al.  On rapid releases and software testing: a case study and a semi-systematic literature review , 2015, Empirical Software Engineering.

[7]  Sergio Segura,et al.  Evolutionary composition of QoS-aware web services: A many-objective perspective , 2017, Expert Syst. Appl..

[8]  Sebastián Ventura,et al.  Interactive multi-objective evolutionary optimization of software architectures , 2018, Inf. Sci..

[9]  Torgeir Dingsøyr,et al.  Emerging themes in agile software development: Introduction to the special section on continuous value delivery , 2016, Inf. Softw. Technol..

[10]  Ignacio Blanquer,et al.  Dynamic Management of Virtual Infrastructures , 2015, Journal of Grid Computing.

[11]  Fabiano Cutigi Ferrari,et al.  The impact of Software Testing education on code reliability: An empirical assessment , 2017, J. Syst. Softw..

[12]  Ioan Dragan,et al.  Architecture of a Scalable Platform for Monitoring Multiple Big Data Frameworks , 2016, Scalable Comput. Pract. Exp..

[13]  Erich Schikuta,et al.  A Dynamic Multi-Objective Optimization Framework for Selecting Distributed Deployments in a Heterogeneous Environment , 2011, ICCS.

[14]  Evgeny Nikulchev,et al.  Choosing a Data Storage Format in the Apache Hadoop System Based on Experimental Evaluation Using Apache Spark , 2021, Symmetry.

[15]  S. Justus,et al.  Cloud Testing Tools and its Challenges: A Comparative Study , 2015 .

[16]  Vahid Garousi,et al.  Worlds Apart: Industrial and Academic Focus Areas in Software Testing , 2017, IEEE Software.

[17]  Souhwan Jung,et al.  Centralized management solution for vagrant in development environment , 2017, IMCOM.

[18]  Ioannis Konstantinou,et al.  Cloud application deployment with transient failure recovery , 2018, Journal of Cloud Computing.

[19]  Dirk Beyer,et al.  Software Verification: Testing vs. Model Checking - A Comparative Evaluation of the State of the Art , 2017, Haifa Verification Conference.

[20]  Matteo Ferroni,et al.  Performance-aware load shedding for monitoring events in container based environments , 2019, SIGBED.

[21]  В. Я. Цветков,et al.  Поддержка жизненного цикла программных компонент , 2020 .

[22]  Gregor Engels,et al.  Usability Evaluation of Model-Driven Cross-Device Web User Interfaces , 2018, HCSE.

[23]  Nikolay Laptev,et al.  Digital Psychological Platform for Mass Web-Surveys , 2020, Data.

[24]  Peter Gorm Larsen,et al.  Enabling continuous integration in a formal methods setting , 2019, International Journal on Software Tools for Technology Transfer.

[25]  Azubuike Ezenwoke,et al.  QoS-based ranking and selection of SaaS applications using heterogeneous similarity metrics , 2018, J. Cloud Comput..