arXiv subjects

Hasan Abdulla

Publications and source records attributed to Hasan Abdulla.

3 recordsLinked to original sources

arXiv2026

DevOps and General Developers: Insights from Stack Overflow's 2023 Survey

Purpose: To investigate the distinct roles of DevOps specialists and general software developers, examining their varying use of tools, technologies, methodologies, and demographics in the current software development environment. In addition, to differentiate these two professional groups regarding their unique contributions and challenges in the field. Design/Methodology/Approach: The research uses a quantitative approach to analyze data from the Stack Overflow 2023 Developer Survey. It focuses on a comparative analysis of technological preferences, demographic information, and professional experiences between DevOps specialists and general developers, highlighting key trends and differences. The data analysis was conducted using Python's Pandas library for data analysis. Findings: The research indicates no significant difference in the tool and technology preferences between DevOps specialists and general software developers, highlighting their complementary roles. DevOps specialists and general software developers use tools like Docker and Kubernetes, emphasizing efficiency and automation. While general developers employ diverse tools for various role demands, demographic trends show younger general developers and mid-career DevOps professionals. This age range reflects growing experience in DevOps, and both groups are adapting to remote and hybrid work models in the evolving tech industry. Practical Implications: This research offers perspectives on the dynamic roles within software development, emphasizing the growing importance of DevOps. It is a valuable resource for academic and industry professionals to understand the evolving dynamics in software development roles. Originality/Value: This research fills a significant gap in the existing literature regarding the evolving dynamics of software development roles.

cs.SE↗

arXiv2026

An SOA-Based Big Data Management Framework for Primary Healthcare Centers in Bahrain

The rapid data growth in the healthcare industry has presented significant challenges in managing big data effectively. This research designs and applies a service-oriented architecture-based approach for managing big data for a Primary Healthcare Center in Bahrain (SOA-MHC) to investigate the application of SOA principles in enterprise architecture. The research employs the Design Science Research Methodology (DSRM) to guide the development of the SOA-MHC artifact, following its six systematic phases: problem identification, objective definition, design/development, demonstration, evaluation, and communication. The SOA-MHC utilizes a layered architecture comprising a Presentation Layer, Service Layer, and Data Integration Layer. The framework leverages core SOA principles - specifically loose coupling, reusability, and interoperability - to create a modular system capable of handling the complexities of healthcare big data. Diverse data collection and validation methods were utilized, including stakeholder interviews via the Delphi technique, document reviews, observations, and benchmarking research. The study's empirical findings highlight significant operational benefits from the SOA-MHC implementation. These include improved data integration and interoperability across disparate systems, enhanced real-time data analytics and clinical insights, clear operational efficiency gains, cost reductions, and elevated patient engagement and satisfaction. The architecture demonstrates the practical efficacy of an SOA-based enterprise framework in mitigating fragmented data silos. This research contributes to the knowledge of applying SOA principles in medical informatics and provides a validated deployment roadmap for practitioners and researchers transforming data management methods in healthcare institutions.

cs.SE↗

arXiv2025

Android Malware Detection: A Machine Leaning Approach

This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android applications and analyzes their accuracy, efficiency, and real-world applicability. Key findings show that ensemble methods demonstrate superior performance, but there are trade-offs between model interpretability, efficiency, and accuracy. Given its increasing threat, the insights guide future research and practical use of ML to combat Android malware.

cs.CR↗