Software Development for Computers That Are Brand New

Year : 2025 | Volume : 12 | Issue : 02 | Page : 07 18
    By

    V. Basil Hans,

  1. Research Professor, Department of Management and Commerce, Srinivas University, Mangaluru, Karnataka, India

Abstract

The purpose of this study is to present a novel method to software development that is intended to answer the issues that are constantly changing in the realm of computer programming and application development. The demand for software solutions that are superior in terms of efficiency, scalability, and adaptability has substantially increased in tandem with the rapid advancement of technology. The development approach that has been offered integrates contemporary methods such as cloud-based architecture, machine learning integration, and agile frameworks. The goal of this methodology is to streamline the development process while simultaneously improving application performance and the user experience. This innovative software development method is described in detail in the study, which highlights its most important characteristics and advantages, such as enhanced cooperation, faster deployment cycles, and higher flexibility for customizing features. In addition, we give case examples that demonstrate the effective implementation of this technique in real-world applications. These case studies highlight the strategy’s capacity to satisfy the requirements of both developers and end-users. By means of this innovation, we intend to establish a new standard for the production of software that is both future-proof and in line with the most recent technological breakthroughs.

Keywords: Real world applications, developing software, technologies, communication, continuous integration

[This article belongs to Journal of Open Source Developments ]

How to cite this article:
V. Basil Hans. Software Development for Computers That Are Brand New. Journal of Open Source Developments. 2025; 12(02):07-18.
How to cite this URL:
V. Basil Hans. Software Development for Computers That Are Brand New. Journal of Open Source Developments. 2025; 12(02):07-18. Available from: https://journals.stmjournals.com/joosd/article=2025/view=222469


References

  1. Parsons D, MacCallum K. Agile and lean concepts for teaching and learning. Agile and Lean Concepts for Teaching and Learning. Singapore: Springer; 2019. https://doi. org/10.1007/978-981-13-2751-3.
  2. Razzak MA. An Empirical Study on Leanness and Flexibility in Distributed Software Development. arXiv preprint arXiv:1711.01097. 2017 Nov 3.
  3. Schtein IA. Management Strategies for Adopting Agile Methods of Software Development in Distributed Teams. Dissertation. Minnesota: Walden University; 2018.
  4. Shahin M, Babar MA, Zhu L. Continuous integration, delivery and deployment: a systematic review on approaches, tools, challenges and practices. IEEE Access. 2017 Mar 22; 5: 3909–43.
  5. Proulx A, Raymond F, Roy B, Petrillo F. Problems and solutions of continuous deployment: A systematic review. arXiv preprint arXiv:1812.08939. 2018 Dec 21.
  6. Wiedemann A, Wiesche M, Gewald H, Krcmar H. Implementing the planning process within DevOps teams to achieve continuous innovation. Proceedings of the 52nd Hawaii International Conference on System Sciences. 2019.
  7. Dingsøyr T, Falessi D, Power K. Agile development at scale: the next frontier. IEEE Softw. 2019 Feb 21; 36(2): 30–8.
  8. Almeida F. Bibliometric analysis of agile software development. arXiv preprint arXiv:2004.05876. 2020 Apr 13.
  9. Alenezi M. Factors Hindering the Adoption of DevOps in the Saudi Software Industry. arXiv preprint arXiv:2204.09638. 2022 Apr 5.
  10. Bildiri F, Akdemir Ö. From agile to devops, holistic approach for faster and efficient software product release management. AYBU Business Journal (ABJ). 2021 Dec 23; 1(1): 26–33.
  11. Khan AA, Khan JA, Akbar MA, Zhou P, Fahmideh M. Insights into software development approaches: mining Q &A repositories. Empir Software Eng. 2024 Jan; 29(1): 8.
  12. Wessel M, Mens T, Decan A, Mazrae PR. The github development workflow automation ecosystems. In Software Ecosystems: Tooling and Analytics. Cham: Springer International Publishing; 2023 May 26; 183–214.
  13. Li K, Zhu A, Zhao P, Song J, Liu J. Utilizing deep learning to optimize software development processes. arXiv preprint arXiv:2404.13630. 2024 Apr 21.
  14. Larrucea X, Fernandez R, Soriano J, Martínez AL, Gonzalez-Barahona JM. A service based development environment on Web 2.0 platforms. In: Towards a Service-Based Internet: First European Conference, ServiceWave 2008, Madrid, Spain, December 10-13, 2008. Proceedings 1. Berlin Heidelberg: Springer; 2008; 38–48.
  15. Ma Z. Current applications and future prospects of artificial intelligence in software engineering. Advances in Engineering Innovation (AEI). 2024 Nov 19; 13: 71–5.
  16. Zheng XR, Lu Y. Blockchain technology–recent research and future trend. Enterp Inf Syst. 2022 Dec 2; 16(12): 1939895.
  17. Sieferd E. Decoded: Exploring user involvement in the early stages of software development. Thesis. USA: Indiana University; 2017 May.
  18. Dumas JS, Salzman MC. Usability assessment methods. Rev Hum Factors Ergon. 2006 Apr; 2(1): 109–40.
  19. Brandell JR, Varkas T. Narrative case studies. The handbook of social work research methods. Thousand Oaks, California: Sage Publications; 2001; 293–307.
  20. Dingsøyr T, Falessi D, Power K. Agile development at scale: the next frontier. IEEE Softw. 2019 Feb 21; 36(2): 30–8.
  21. Mendes Calo K, Estévez EC, Fillottrani PR. A quantitative framework for the evaluation of Agile Methodologies. J Comput Sci Technol. 2010; 10(2): 68–73.
  22. Hage B. Analysis and Comparison of Multiple Approaches for Software Development Management as Applied to a Design Studio Project. Undergraduate Honors Thesis. USA: University of Nebraska-Lincoln; 2019.
  23. Khalid MA, Naeem MA. A Surwey of Automated Testing Tools. VFAST Trans Softw Eng. 2018 Sep 30; 6(1): 15–21.
  24. Banerjee I, Nguyen B, Garousi V, Memon A. Graphical user interface (GUI) testing: Systematic mapping and repository. Inf Softw Technol. 2013 Oct 1; 55(10): 1679–94.
  25. Khan AZ, Iftikhar SY, Bokhari RH, Khan ZI. Issues/challenges of automated software testing: A case study. Pak J Comput Inf Syst. 2018; 3(2): 61–75.
  26. de Souza Santos R, Fronchetti F, Freire S, Spinola R. Software Fairness Debt: Building a Research Agenda for Addressing Bias in AI Systems. ACM Transactions on Software Engineering and Methodology. 2025 May 26; 34(5): 132(21p).
  27. Santos RD, Fronchetti F, Freire S, Spinola R. Software Fairness Debt. arXiv preprint arXiv:2405.02490. 2024 May 3.

Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 07/03/2025
Accepted 22/03/2025
Published 13/06/2025
Publication Time 98 Days



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