Recent Trends in Computer Science and Software Technology ISSN: 3139-1389 (Online)

Authors: Shreya Arudkar, Amit Pimpalkar

Abstract: Nowadays IT corporations area unit payment over 45% of their value in fixing code bugs. Historically these bugs area unit fastened by manual assignment to a selected developer; this approach causes an excessive amount of dependency. The new and various approach is that the Bug Tracking System, that fixes the bug and assigns the reportable bug to a developer mechanically in order that it decreases the time and price in manual work. Mix instance selection with feature selection to at the same time cut back information scale on the bug dimension and therefore the word dimension. we have a tendency to propose to use machine learning technique in bug sorting to predict that developer ought to be assigned on the bug, supported its description by applying text categorization.

Author: Arjun Sharma, Neha Verma.

Abstract: The rapid evolution of software development has been significantly influenced by the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. These advancements enable automation, predictive analytics, code optimization, and enhanced decision-making in the software development lifecycle. This paper investigates the applications of AI and ML in modern software development, emphasizing automated code generation, bug detection, performance optimization, and intelligent project management. By reviewing existing frameworks, algorithms, and tools, this research highlights the potential of AI-driven approaches to revolutionize software engineering practices. The study also discusses challenges, including model interpretability, data dependency, and ethical considerations, providing insights into future research directions for sustainable and efficient software development.

Keywords: Artificial Intelligence, Machine Learning, Software Development, Automated Code Generation, Predictive Analytics, Intelligent Systems.

Author: Dr. Arjun Malhotra, Ms. Riya Sen. 

Abstract: Software supply chain security has emerged as a critical concern in modern cybersecurity frameworks, as software products increasingly rely on complex ecosystems of third-party components, libraries, and cloud-based services. Compromises in these supply chains can lead to severe breaches, data theft, and operational disruptions. This paper examines best practices for securing the software supply chain, integrating risk assessment models, continuous monitoring, cryptographic verification, and policy compliance into a unified strategy. Emphasis is placed on regulatory frameworks, industry standards, and advanced threat intelligence. Practical case studies highlight how organizations can enhance resilience against malicious code injections, dependency hijacking, and insider threats. The discussion further outlines the importance of secure coding, vendor vetting, software bill of materials (SBOM) adoption, and automated vulnerability scanning. The proposed methodology offers a layered defense model to minimize attack surfaces, ensuring that both proprietary and open-source components meet stringent security standards. This research provides actionable recommendations for organizations seeking to build robust, verifiable, and trustworthy software ecosystems.

Keywords: Software supply chain, cybersecurity, SBOM, vulnerability scanning, cryptographic verification, dependency security, threat intelligence.

Author: Dr. Neha S. Verma, Rajeev K. Sharma.

Abstract: The rapid advancements in quantum computing pose an unprecedented threat to classical cryptographic systems that currently safeguard global digital communications. Traditional algorithms like RSA and ECC rely on computational hardness assumptions that can be efficiently broken by quantum algorithms such as Shor’s and Grover’s. Quantum-safe cryptography (QSC), also called post-quantum cryptography, aims to design algorithms resilient to both classical and quantum attacks, ensuring the long-term security of sensitive data. This paper explores the principles of QSC, its mathematical foundations, leading algorithm families, standardization efforts, and practical deployment strategies. A comparative table outlines key features of prominent post-quantum algorithms, and the paper concludes with an analysis of current research challenges and potential integration pathways into existing infrastructures.

Keywords: Quantum-safe cryptography, post-quantum security, lattice-based cryptography, quantum computing threats, NIST PQC, hash-based signatures, multivariate cryptography.

Author: Dr. Rohan Mehta, Ananya Rao.

Abstract: In an era where data has emerged as the most valuable asset, ensuring its security without compromising utility is a pressing challenge. Privacy-enhancing computation techniques, particularly homomorphic encryption and secure enclaves, are reshaping the data security landscape. Homomorphic encryption allows computation directly on encrypted data, producing results that, when decrypted, match those obtained from plaintext operations. Secure enclaves, on the other hand, provide hardware-level isolation to safeguard sensitive computations from external interference. This paper explores the fundamental principles, recent advancements, practical use cases, and challenges associated with these technologies. Emphasis is placed on their synergistic potential in healthcare, finance, and cloud computing environments, along with a comparative analysis to guide adoption strategies. The paper concludes by discussing future directions for achieving scalable, interoperable, and quantum-resistant privacy-preserving systems.

Keywords: — Privacy-enhancing computation, homomorphic encryption, secure enclaves, cloud security, confidential computing, data privacy, cryptography.

Author: Dr. Ananya Mehta, Mr. Rohan S. Kapoor.

Abstract: Serverless computing has emerged as a transformative paradigm for deploying applications without the need for direct infrastructure management. It offers advantages such as automatic scaling, reduced operational overhead, and cost efficiency. However, the dynamic and event-driven nature of serverless systems introduces challenges related to performance optimization, cold starts, resource allocation, and integration with legacy systems. This paper explores advanced optimization techniques to enhance serverless architecture efficiency. Strategies including function-level tuning, provisioning models, event orchestration, caching mechanisms, and hybrid deployments are discussed. Additionally, we propose a comparative performance evaluation framework and cost-benefit model for selecting optimal serverless configurations. The findings indicate that well-structured optimization techniques can lead to significant performance gains and cost savings, making serverless architectures more sustainable for long-term enterprise adoption.

Keywords: Serverless Computing, Cloud Optimization, Function-as-a-Service, Cold Start Mitigation, Event Orchestration, Hybrid Cloud.

Author: Dr. Anita Mehra, Rahul Sharma.

Abstract: Model-Driven Software Development (MDSD) has emerged as a paradigm that emphasizes the use of high-level models as primary artifacts in the software engineering process. It aims to shift the focus from low-level coding to the design and transformation of abstract models that can be automatically translated into executable code. This approach has gained traction due to its potential to improve productivity, maintainability, and adaptability, especially in complex enterprise systems. This paper explores the foundational principles, benefits, challenges, and tools of MDSD, highlighting its role in enabling rapid adaptation to evolving business requirements. Furthermore, the paper examines case studies and industry applications, emphasizing the integration of model-driven techniques into agile and DevOps workflows. The discussion concludes with future trends and research opportunities in this field.

Keywords: Model-Driven Software Development, MDSD, Code Generation, UML, Model Transformation, Software Engineering, Automation.

Author: Dr. Ananya Mehta, Rahul Verma.

Abstract: Hybrid and multi-cloud infrastructure management has emerged as a pivotal strategy for enterprises aiming to maximize flexibility, reduce vendor lock-in, and enhance system resilience. This paper examines the architecture, deployment strategies, and governance frameworks necessary for effective hybrid and multi-cloud environments. By integrating public and private cloud services, organizations can balance scalability with compliance, cost optimization, and performance requirements. The discussion also explores automation tools, monitoring systems, and security considerations that enable sustainable multi-cloud operations. Practical implementation guidelines, challenges, and case study examples are provided to assist IT decision-makers in optimizing their hybrid-cloud journey.

Keywords: Hybrid cloud, Multi-cloud, Infrastructure management, Cloud governance, Automation, Cloud orchestration, Cloud monitoring, Vendor lock-in prevention.

Author: Dr. Priyanka Malhotra, Mr. Ankit Verma.

Abstract: The exponential growth of digital data, coupled with the need for immediate insights, has propelled real-time big data stream processing to the forefront of modern computing. Unlike traditional batch processing, real-time stream processing enables the analysis, transformation, and storage of continuous data flows within milliseconds. This paper explores the foundational concepts, architectures, and technologies underpinning real-time big data stream processing. It examines industry-leading frameworks such as Apache Kafka, Apache Flink, and Spark streaming, along with use cases in finance, healthcare, and IoT. Additionally, it addresses the challenges of scalability, fault tolerance, latency, and exactly-once processing, and proposes best practices for building reliable, high-performance systems.

Keywords: Real-Time Processing, Big Data, Stream Processing, Apache Kafka, Low Latency, Data Pipelines

Author: Dr. Priyanka Nair, Amit Bansal.

Abstract: Federated Learning (FL) is a decentralized machine learning paradigm that enables multiple clients to collaboratively train a shared model without exchanging raw data. This privacy-preserving approach addresses growing concerns about data security and regulatory compliance, particularly under frameworks such as GDPR and HIPAA. By transmitting only model updates, FL ensures that sensitive information remains local while still benefiting from collective intelligence. This paper examines the architecture, advantages, challenges, and real-world applications of federated learning, along with emerging strategies to enhance its security and efficiency.

Keywords: Federated Learning, Privacy-Preserving AI, Distributed Machine Learning, Data Security, Edge AI, GDPR Compliance, Secure Aggregation

Author: Dr. Ananya Gupta, Rohit Verma

Abstract: Ethical Artificial Intelligence (AI) is an evolving discipline that focuses on ensuring fairness, accountability, and transparency in AI-powered software applications. As AI becomes increasingly embedded in decision-making systems, the risks of algorithmic bias and unintended discrimination have drawn significant attention from policymakers, researchers, and industry leaders. Bias in AI systems can arise from skewed training data, flawed model design, or systemic inequalities reflected in digital environments. This paper explores the importance of ethical AI, methods for detecting and mitigating bias, real-world case studies, and future directions for building more equitable AI systems that respect human rights and societal norms.

Keywords: Ethical AI, Bias Mitigation, Fairness in AI, Responsible AI, Algorithmic Accountability, AI Ethics, Transparency.

Author: Dr. Aditi Sharma, Mr. Rohan Mehta.

Abstract: Artificial Intelligence (AI) systems are transforming industries across the globe, yet the complexity and unpredictability inherent in AI algorithms pose significant challenges to software quality assurance (SQA). This paper presents a comprehensive exploration of SQA practices tailored for AI systems, highlighting the need for rigorous validation, transparency, and ethical considerations. The study outlines key methodologies, evaluation metrics, and process models to ensure AI systems meet performance, reliability, and fairness standards. Special emphasis is placed on explainable AI, data quality, and continuous monitoring in deployment phases.

Keywords: Software Quality Assurance, Artificial Intelligence, Explainable AI, Validation, Testing, Ethical AI

Author: Dr. Rohan Mehta, Ananya Kapoor

Abstract: 0.9 cm indentation on both sides, italics, justified. Data visualization has emerged as a critical tool in the modern enterprise decision-making process. With growing data complexity, visual representation bridges the gap between raw datasets and actionable insights, enabling executives and operational teams to identify patterns, monitor performance, and predict trends with greater accuracy. This paper explores the methodologies, technologies, and best practices for effective data visualization within an enterprise setting. It also examines the challenges enterprises face in adopting visualization tools, the role of artificial intelligence in enhancing visualization capabilities, and case studies illustrating successful implementations. The findings highlight that well-designed visualizations not only accelerate decision-making but also reduce cognitive overload, fostering a data-driven culture across organizations.

Keywords: Data Visualization, Decision-Making, Business Intelligence, Dashboards, Analytics, Enterprises, AI-Driven Visualization.

Author: Dr. Aarav Mehta, Ms. Riya Sharma

Abstract: Containerization has emerged as a transformative technology in enterprise application deployment, enabling rapid scalability, portability, and consistency across diverse computing environments. Kubernetes, as the leading container orchestration platform, has revolutionized how organizations manage large-scale containerized workloads. This paper explores the integration of containerization and Kubernetes in enterprise environments, detailing their architectural principles, deployment strategies, performance benefits, and security considerations. The discussion includes the role of Kubernetes in automating deployment, scaling, and management of containerized applications, along with case studies of enterprise adoption. Finally, best practices and future trends are presented to guide organizations toward sustainable and efficient container orchestration.

Keywords: Containerization, Kubernetes, Docker, Microservices, Orchestration, Enterprise Applications, Cloud Computing.

Author: Dr. Ananya Mehra, Mr. Raghav Sharma

Abstract: Cloud-native application development has emerged as a transformative approach for building and deploying scalable, resilient, and agile software solutions. By leveraging containerization, microservices, continuous integration/continuous deployment (CI/CD), and orchestration tools like Kubernetes, organizations can accelerate delivery cycles while optimizing resource usage. This paper provides a comprehensive analysis of cloud-native application development and orchestration, highlighting architectural principles, enabling technologies, and industry best practices. Furthermore, it examines challenges related to security, scalability, and interoperability, and proposes strategies for optimizing orchestration processes to meet enterprise demands. The findings underscore the importance of adopting DevOps practices, selecting suitable orchestration frameworks, and implementing automated monitoring to enhance operational efficiency in modern cloud environments.

Keywords: Cloud-native applications, orchestration, Kubernetes, microservices, DevOps, CI/CD, containerization, scalability.

Author: Vikas Sharma, Priya Mehta

Abstract: Continuous Integration/Continuous Deployment (CI/CD) has emerged as a fundamental practice in modern software engineering, enabling rapid, reliable, and automated software delivery. This paper presents a comprehensive analysis of CI/CD automation, highlighting its significance, core components, and the orchestration tools that enhance productivity. The study covers the integration pipeline’s stages, the role of version control, automated testing, containerization, and deployment strategies. A tabulated comparison of leading CI/CD tools is provided, along with a discussion on the challenges and future trends in automation. The research concludes that a well-implemented CI/CD pipeline shortens release cycles, improves code quality, and accelerates innovation in software projects.

Keywords: Continuous Integration, Continuous Deployment, CI/CD Pipeline, Automation, DevOps, Orchestration, Software Delivery.