Anujkumarsinh Donvir

The complexity, scale, and performance requirements of distributed applications continue to increase in the current era. This makes the launch of applications challenging, while keeping consistently high operational costs for enterprises. This necessitates a robust Operational Readiness (OR) strategy that will serve as guidance for enterprises to ensure successful application launches. This study provides a framework for achieving operational excellence, categorizing both frontend and backend systems. This paper also outlines how Artificial Intelligence (AI) has contributed to enhancing the Operational Readiness of highly scalable applications. This study provides actionable insights for software engineers and leaders to launch reliable, highly available, high-performance, and scalable applications in complex cloud environments. Furthermore, the study also addresses the requirements for security and privacy compliance that are crucial for maintaining customer trust, safeguarding sensitive data, and ensuring regulatory adherence. This paper’s recommendations aim to guide future research on improving operations of cloud applications, contributing to the advancement of operations and keeping the post-launch operations cost-effective.

Cloud services are helping with the management and organizational placement of organizational IT resources by providing products that are malleable, versatile, and inexpensive. But as people start using cloud-based applications in areas that cannot afford any disruption in healthcare, finance, public safety and defense, the need for sturdy and reliable systems becomes critical. For critical business processes, performance disruption, availability problem or failure conditions, risk exposure that causes loss, brand deterioration, or death is not acceptable. Durability architectures are built to avoid, accommodate and restore disruptions resulting from equipment breakdown, cyber- attacks, calamities or surge in workload. This paper focuses on fault tolerance, replication of data, disaster recovery, and distributed systems, as methods that can be used to construct reliable systems. It also explores newer trends like Artificial Intelligence, Machine Learning, and Edge Computing that allow the continuous monitoring, analytics, and automated recovery. The issues are presented and discussed in detail, and the trends that include on-premise and cloud environments, compliance issues and evolving threat landscape are highlighted. Specific steps to increase business productivity when using business intelligence, credibility of information, and adaptability are described. This work positions resilience as a competitive advantage underpinning the capabilities of cloud-oriented organisations to navigate the challenges of current technology environments cohesively.

The increasingly complex nature of cloud computing ecosystems used for highly scalable distributed applications requires advanced methodologies in optimizing resource distribution and workload prediction while achieving the optimal level of performance with reduced expenditure. Traditional approaches often become impractical in addressing the erratic nature of cloud workloads, resulting in inefficiency and potential overspending on costs for the enterprises. This research explores deep learning techniques like RNNs, LSTMs, GRUs and DNNs aimed at enhancing predictive capabilities for optimal cloud optimization regarding cost control, workload forecasting, and performance enhancement. However, the opaque nature of many artificial intelligence models is a significant barrier to their widespread adoption in critical cloud management tasks. The lack of transparency in AI-driven decisions can create concerns about trust, accountability, and compliance with regulatory standards. By using Explainable AI (XAI), cloud providers and users can obtain explanations of how AI models predict workload patterns, delineate potential bottlenecks, and offer the best configurations for resources. This kind of transparency promotes trust and effective oversight of cloud operations. This paper reviews a few of the most prevalent methods: SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), which are used to understand the factors that drive AI-based decisions for cloud optimization.

Artificial Intelligence (AI) has revolutionized and transformed the landscape of software development and testing by introducing new efficiencies and capabilities through advancements like Generative AI (GenAI) and Large Language Models (LLMs). While these technologies bring major benefits in terms of productivity, personalization, and innovation, they also raise critical ethical challenges, such as biases, lack of transparency, data privacy concerns, and potential negative societal impacts. This paper examines the ethical considerations involved in developing such advanced AI systems as well using AI systems within software development and testing. It explores existing ethical frameworks and principles provided by leading organizations, emphasizing core concepts like human-centered design, accountability, transparency, fairness, and privacy. Practical strategies for integrating ethical practices throughout the AI development lifecycle are discussed, with a strong emphasis on the need for continuous ethical evaluation. The paper explores the ethical landscape of AI in software development, addressing challenges like algorithmic bias, data security, and broader societal impacts. Real-world case studies presented in the paper demonstrate the consequences of neglecting ethical considerations. Looking forward, the paper suggests future directions, including the development of unified ethical standards, collaborative ethical auditing, regulatory advancements, and higher societal engagement.

This research addresses the critical need for accessible medical imaging for visually impaired doctors and patients. Current methods for converting medical images into tactile formats are time-consuming, expensive, and often lack sufficient detail for accurate diagnosis. This study presents a novel AI-powered system for automated generation of tactile and 3D-printable representations of medical X-ray images. The system employs image processing techniques, including depth estimation and image enhancement, to create high-resolution depth maps suitable for tactile rendering. The system's performance is evaluated using objective metrics (accuracy, processing time) and will be further assessed through user feedback from visually impaired healthcare professionals. This research demonstrates the feasibility of an automated solution for improving access to medical imaging, potentially enhancing diagnostic accuracy and promoting inclusivity in healthcare. Future work will focus on expanding the system to other imaging modalities and optimizing for real-time clinical use.

JavaScript is a leading programming language for mobile and web application development, and frameworks developed for various kinds of testing using JavaScript and play a crucial role in ensuring software reliability and quality assurance. This paper pens a comparative review of popular JavaScript unit testing frameworks Jest, Mocha, and Jasmine and end-to-end (E2E) testing frameworks Cypress, Playwright, and Puppeteer. Paper evaluates these frameworks based on criteria such as ease of use, feature sets, community support, scalability, and integration with CI/CD pipelines. The aim of the paper is to create practical guidelines for web and mobile application unit and E2E testing, and equip development teams with thorough understanding of frameworks for their current and future projects.

This paper provides a comprehensive review of the role of Generative AI (GenAI) tools in modern software application development. It highlights the advancements in machine learning, natural language processing, and neural network architectures that have enabled the evolution of GenAI tools from basic code generation and assistance to fully autonomous software development capabilities. Key GenAI tools like GitHub Copilot, TabNine, Cursor AI and Devin AI are examined, with a focus on their functionalities such as code generation, testing, debugging, and deployment. The paper also discusses the productivity benefits, limitations, and ethical considerations of integrating GenAI tools into the development lifecycle. The aim is to equip software developers, managers, and organizations with a deep understanding of the practical applications and future directions of GenAI in streamlining development processes.