Performance Assessment of Traditional Software Development Methodologies and DevOps Automation Culture

Authors

  • P. Narang Department of Computer Science and Applications, Maharshi Dayanand University, India
  • P. Mittal Department of Computer Science and Applications, Maharshi Dayanand University, India
Volume: 12 | Issue: 6 | Pages: 9726-9731 | December 2022 | https://doi.org/10.48084/etasr.5315

Abstract

Successful implementations of Software Development Methodologies significantly improve software efficiency, collaboration and security. Most companies are moving away from traditional development methodologies towards DevOps for faster and better software delivery. DevOps, which is a primary need of the IT industry, brings development and operation teams together to overcome communication gaps responsible for software failures. It relies on different sets of automation tools to robotize the tasks of software development from continuous integration, to testing, delivery, and deployment. The existence of several automation tools in each development phase raises the need for an integrated set of tools to reduce development time. For this purpose, we used the DevOps-based hybrid model Integrated Tool Chain (ITC), along with three sample java-based projects or code repositories to quantify the results. This paper evaluates and compares measurement metrics of java projects using traditional development methodologies and DevOps, and the results are shown in tabular and graphical format. The latest Google and Stack Overflow Trends have also been included to retrieve the best performer development methodology. This comparative and evaluative performance analysis will be beneficial to young researchers that study the metrics of software development, while also they will be introduced to the automotive environment of DevOps, the latest emerging buzzword in software development.

Keywords:

automation, automation tools, integrated tool chain, software development, DevOps

Downloads

Download data is not yet available.

References

P. Narang and P. Mittal, "Hybrid model for software development: an integral comparison of DevOps automation tools," Indonesian Journal of Electrical Engineering and Computer Science, vol. 27, no. 1, pp. 456–465, Jul. 2022. DOI: https://doi.org/10.11591/ijeecs.v27.i1.pp456-465

P. Narang and P. Mittal, "Implementation of DevOps based Hybrid Model for Project Management and Deployment using Jenkins Automation Tool with Plugins," International Journal of Computer Science and Network Security, vol. 22, no. 8, pp. 249–259, Aug. 2022.

M. Gomes, R. Pereira, M. Silva, J. B. de Vasconcelos, and Á. Rocha, "KPI’s for Evaluation of DevOps Teams," in Information Systems and Technologies, Cham, 2022, pp. 142–156. DOI: https://doi.org/10.1007/978-3-031-04829-6_13

W. W. Rovce, "Managing the Development of Large Software Systems," in Technical Papers of Western Electronic Show and Convention, Los Angeles, CA, USA, Aug. 1970.

G. Papadopoulos, "Moving from Traditional to Agile Software Development Methodologies Also on Large, Distributed Projects.," Procedia - Social and Behavioral Sciences, vol. 175, pp. 455–463, Feb. 2015. DOI: https://doi.org/10.1016/j.sbspro.2015.01.1223

T. Dingsøyr, N. B. Moe, T. E. Fægri, and E. A. Seim, "Exploring software development at the very large-scale: a revelatory case study and research agenda for agile method adaptation," Empirical Software Engineering, vol. 23, no. 1, pp. 490–520, Feb. 2018. DOI: https://doi.org/10.1007/s10664-017-9524-2

A. Agrawal, Mohd. A. Atiq, and L. S. Maurya, "A Current Study on the Limitations of Agile Methods in Industry Using Secure Google Forms," Procedia Computer Science, vol. 78, pp. 291–297, Jan. 2016. DOI: https://doi.org/10.1016/j.procs.2016.02.056

A. Mishra and Z. Otaiwi, "DevOps and software quality: A systematic mapping," Computer Science Review, vol. 38, Nov. 2020, Art. no. 100308. DOI: https://doi.org/10.1016/j.cosrev.2020.100308

P. Debois, "Agile Infrastructure and Operations: How Infra-gile are You?," in Agile 2008 Conference, Toronto, ON, Canada, Dec. 2008, pp. 202–207. DOI: https://doi.org/10.1109/Agile.2008.42

A. A. Khan and M. Shameem, "Multicriteria decision-making taxonomy for DevOps challenging factors using analytical hierarchy process," Journal of Software: Evolution and Process, vol. 32, no. 10, 2020, Art. no. e2263. DOI: https://doi.org/10.1002/smr.2263

M. Ramzan, M. S. Farooq, A. Zamir, W. Akhtar, M. Ilyas, and H. U. Khan, "An Analysis of Issues for Adoption of Cloud Computing in Telecom Industries," Engineering, Technology & Applied Science Research, vol. 8, no. 4, pp. 3157–3161, Aug. 2018. DOI: https://doi.org/10.48084/etasr.2101

L. Leite, C. Rocha, F. Kon, D. Milojicic, and P. Meirelles, "A Survey of DevOps Concepts and Challenges," ACM Computing Surveys, vol. 52, no. 6, Aug. 2019, Art. no. 127. DOI: https://doi.org/10.1145/3359981

D. Trihinas, A. Tryfonos, M. D. Dikaiakos, and G. Pallis, "DevOps as a Service: Pushing the Boundaries of Microservice Adoption," IEEE Internet Computing, vol. 22, no. 3, pp. 65–71, Feb. 2018. DOI: https://doi.org/10.1109/MIC.2018.032501519

K. Aldriwish, "A Deep Learning Approach for Malware and Software Piracy Threat Detection," Engineering, Technology & Applied Science Research, vol. 11, no. 6, pp. 7757–7762, Dec. 2021. DOI: https://doi.org/10.48084/etasr.4412

M. F. Hyder and M. A. Ismail, "INMTD: Intent-based Moving Target Defense Framework using Software Defined Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5142–5147, Feb. 2020. DOI: https://doi.org/10.48084/etasr.3266

M. A. Silva, "Productivity Gains of DevOps Adoption in an IT Team: A Case Study," in 27th International Conference on Information Systems Development, Lund, Sweden, 2018.

J. Stoneham, P. Thrasher, T. Potts, H. Mickman, and C. DeArdo, DevOps Case Studies: The Journey To Positive Business Outcomes. IT Revolution.

Downloads

How to Cite

[1]
P. Narang and P. Mittal, “Performance Assessment of Traditional Software Development Methodologies and DevOps Automation Culture”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 6, pp. 9726–9731, Dec. 2022.

Metrics

Abstract Views: 389
PDF Downloads: 344

Metrics Information
Bookmark and Share