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Modern application lifecycle management approaches

Zajączkowska Patrycja

Studia Informatica : systems and information technology

2025

vol. 1(32)

31--45

Effective application lifecycle management (ALM) relies on modern tools and practices that streamline development while ensuring security and stability. This article examines key components of a modern ALM strategy, including DevOps culture, CI/CD pipelines, Infrastructure as Code, containerization, orchestration, monitoring, observability, and the growing role of AI. Special attention is given to the integration of security into the CI/CD process, particularly through automated testing techniques such as Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST). These practices help identify vulnerabilities early in the development cycle, reducing risk and enhancing application reliability.Adopting these technologies involves initial investments in licenses, training, and team restructuring. However, these efforts are rewarded through automation of repetitive tasks, accelerated deployment cycles, improved scalability, and proactive issue detection. AI-driven tools further enhance development efficiency through intelligent code suggestions, predictive analytics, and automated bug detection. By embracing these modern approaches, organizations can achieve a more secure, efficient, and agile development process, better equipped to adapt to a rapidly evolving technological landscape.

Optimizing continuous integration and continuous deployment pipelines with machine learning: Enhancing performance and predicting failures

Dileepkumar S.R., Mathew Juby

Advances in Science and Technology. Research Journal

2025

Vol. 19, no. 3

108--120

Continuous Integration and Continuous Deployment (CI/CD) pipelines form the backbone of modern software development but typically suffer from long build times, repeated failures, and inefficient use of resources. This work presents a machine learning-based framework that systematically improves pipeline performance through predictive modelling. More specifically, the work will focus on developing a Support Vector Machine model to predict pipeline failures; it minimizes build times through optimized resource allocation while building dynamic frameworks for continuous improvement of CI/CD pipelines. The study assumes an exhaustive literature review and propounds a new approach by using an SVM model. Critical performance metrics such as the build duration, test pass/fail rates, and resource consumption are analysed and the framework is found to have significant improvements by the measurements: a 33% decrease in the build time, a 60% decrease in the failure rates, and optimization of CPU and memory utilization. The experiments validated the outcome of being scalable in an intelligent manner such that persistent problems with CI/CD are solved in modern DevOps practices. This work provided initial groundwork by bringing in the concept of ML in CI/CD process, aiming to enhance reliability and efficiency in the pipelines that would lead towards major strides in adaptive systems in the context of software engineering workflows.