AI Driven Quality Assurance: A Study using Factor Analysis from the Nepalese IT Industry

Abstract

This study investigates the influence of Artificial Intelligence (AI) on software Quality Assurance (QA) processes within the Nepalese IT sector. A quantitative research design was employed, and data were collected using a structured questionnaire comprising multiple-choice and five-point Likert-scale questions. The sample included 230 QA professionals selected through a purposive sampling method from various IT companies in Nepal. The study was guided by the AI-Driven Quality Assurance Effectiveness Model (AI-QAEM), which integrates the Technology Acceptance Model (TAM) and Quality Management Theory (QMT) to explain how AI adoption impacts QA outcomes. Data analysis using correlation and regression revealed a strong model significance (R² = 0.774, F = 66.752, p < 0.001) and high reliability (Cronbach’s Alpha = 0.906). The KMO value of 0.880 confirms sampling adequacy, while reliability tests show strong internal consistency. Findings indicate that Testing Speed (β = 0.480) and Testing Techniques (β = 0.234) are the most influential factors in improving software quality. Overall, effective AI implementation enhances testing accuracy, efficiency, and product reliability across Nepal’s IT sector.