Skip to content
Open access

Empirical Evaluation of DevOps Implementation in Optimizing the Software Life Cycle for Cloud Platforms

Aug 2026 · International Journal for Research in Applied Science and Engineering Technology · 0 citations

TL;DR

In this project, one will be working on building a Continuous Integration and Continuous Deployment pipeline using Microsoft Azure to automate software delivery and improve overall efficiency.

Abstract

Today, IT organizations, in particular, have to provide quick, reliable and high-quality software solutions for meeting the changing market requirements. While the development process has been structured by traditional software engineering paradigms (Waterfall, Agile and Spiral Models), traditional workflows often involve operational bottlenecks. In particular, the lack of communication and coordination between development and operations can lead to delivery delays. DevOps has come about as a transformative approach that fuses software design and IT operations into a single, streamlined and automated process that aims to overcome these systemic inefficiencies. DevOps is used to streamline the software delivery pipeline, when paired with Cloud Computing infrastructure, including SaaS, PaaS, and IaaS solutions from AWS, Azure, and GCP. In this project, one will be working on building a Continuous Integration and Continuous Deployment (CI/CD) pipeline using Microsoft Azure to automate software delivery and improve overall efficiency

Read PDF

Similar papers

Open access 2026

DevOps Beyond Software: Establishing CI/CD Frameworks across Semiconductor and Cloud Engineering Lifecycles

This paper presents a realistic case study showing how the implementation of DevOps principles in cross-functional teams, with the help of the standardized pipelines and integrated automation, facilitates cooperation, reduces cycle times, enhances quality assurance and accelerates delivery outcomes.

Karthik Allam · 0 citations

IMPLEMENTATION GUIDE OF SOFTWARE DEVELOPMENT BEST PRACTICES BASED ON DEVOPS

A guide that facilitates the step-by-step adoption of five practices: version control, change requests controlled with manual code inspection, continuous integration, static code analysis, and implementing an automated pipeline for continuous integration is proposed.

M. Pastrana, Hugo-Armando Ordoñez-Erazo, C. Cobos-Lozada et al. · 0 citations
Open access 2026

Evaluating the Architectural Impact of Monolithic and Microservices Styles on Operational Efficiency in Modern Software Systems

This paper analyzes the two software architectural approaches namely Monolithic Architecture vs Microservices Architecture when considering scalable software development, and puts the following into consideration: empirical trade-offs, scalability consideration, organizational implications, and development complexity.

Theophilus Bamise Ajala, A. Oduroye, I. Ayoade et al. · 0 citations
Open access Jul 2026

Empirical Evaluation of a DevSecOps Proxy Pipeline for Multi-Tier Web Applications

This research proposes the evaluation of a “proxy” DevSecOps pipeline, defined as an automated intermediary architecture that decouples intensive security scanning from the primary build flow to prevent bottlenecks and demonstrates that security validation is the most time-intensive part of the automated proxy workflow.

Abderrahim Rida, A. Bakhil, Ayoub Ait Lahcen · 0 citations
Review Open access Aug 2026

Software Development Methodologies Evolutionary Trends from Waterfall to DevOps

Agile and DevOps are currently the most popular software development techniques. Software methodologies started with the application of the Waterfall model and other models of Software development. It provides a general overview of the approaches and their integration throughout the Software Development Life Cycle (SDLC). SDLC phases are described and different SDLC methodologies are discussed with respect to their impact on motivation, productivity and quality. A comparison of the traditional Waterfall and newer iterative/incremental/agile is identified, as are the reasons behind this change, such as customer participation, timely feedback, and quality improvement. The following issues are common to all methodologies: architecture limitations, scalability and organizational restrictions. It was identified that the industry tends to follow a hybrid Agile-DevOps practice with more emphasis on automation, continuous integration and collaboration to achieve quick and competitive software environments.

Mr. Raman Kumar   · 0 citations
Open access Aug 2026

Algorithm-driven Development: A proactive approach to improving software quality and reducing defects

Algorithm-Driven Development is introduced, a methodology developed from industrial practice to address recurring challenges in translating requirements into reliable, testable, and maintainable software behavior that provides systematic coverage of functional scenarios from the outset of development.

Philippe Jawish, Pierre Evrard, Alexandre Lemerle et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.