Improving Software Maintenance Offshore Outsourcing Quality Assurance Using Mixed-Methods Analysis

Abstract

The growing dependence on offshore software maintenance outsourcing (OSMO) highlights the necessity of efficient quality assurance systems to guarantee better delivery results. The present quality control frameworks have no connection with contemporary technology such as machine learning, even if outsourcing techniques have advanced. This paper tries to fill in the gaps in the existing literature by utilizing machine learning approaches to assess customer proposals and reduce risks. It also creates a new quality assurance framework for OSMO. Our mixed-methods approach comprised qualitative case studies, industry expert interviews, and numerical analysis. The project employed controlled learning models to assess customer bids and direct decision-making procedures. Information collected from several sources was progressively coded and verified using Rooted Theory. The suggested platform greatly increases the accuracy and dependability of OSMO's job selection and deployment procedures. Among the most important conclusions are the significance of strong risk management, social flexibility, and efficient communication. By highlighting the challenges brought on by linguistic and geographical limitations, the research shows how machine learning may improve decision-making and project outcomes. The case studies also show how resolving cultural and time zone disparities may enhance collaboration and productivity. By executing the suggested quality assurance platform into practice, OSMO procedures will rise dramatically, improving task completion expenses and elevating client happiness. The paper offers insightful analysis and useful recommendations for enhancing quality control in offshore software maintenance.