Test Automation in HR Solutions: A Technical Deep Dive (Published)
Test automation has emerged as a cornerstone capability in modern human resource technology, enabling organizations to deliver reliable, efficient, and user-friendly systems across recruitment, learning, and broader HR domains. This technical deep dive examines how HR solution providers leverage frameworks like Cypress, WebDriver, and Appium alongside CI/CD pipelines to address complex testing challenges unique to HR systems. The integration of artificial intelligence enhances testing effectiveness through visual validation, smart element identification, and predictive failure analysis, while cloud-based implementations facilitate greater scalability and coverage. As HR platforms increasingly process sensitive personal data across multi-tenant architectures, specialized approaches including data anonymization, synthetic generation, and isolation verification have become essential. The evolution toward low-code testing tools, AI-driven test generation, chaos engineering, and shift-right methodologies reflects the growing recognition that quality assurance must directly align with actual user experiences and business requirements, ultimately creating HR systems that deliver meaningful value across increasingly diverse work environments.
Keywords: Automation, Integration, Resilience, Validation, cloud-based
An Improved Model for Financial Institutions Loan Management System: A Machine Learning Approach (Published)
The inability of financial instructions, especially the Microfinance Banks, to forecast for the need of borrowers in order to make provision for them has been a cause for concern. Applications are made and most times the reply is that funds are not available. This paper demonstrated the design and implementation of neural network model for development of an improved loan-based application management system. The back propagation algorithm was used to train the neural network model to ascertain corrections between the data and to obtain the threshold value. The data was collected over a period of three years from UCL machine learning repository. The system was designed using object oriented methodology and implemented with Java programming language and MATLAB. The results obtained showed the mean squared error values 1.09104e-12, 5.56228e-9 and 5.564314e-4 for the training, testing and validation respectively. It was seen from the result that neural network can forecast the financial market with minimum error.
Keywords: Mean Square Error, Neural network, Regression, Validation, and Forecasting.