Human–AI Collaboration in Software Quality Assurance: Balancing Automation and Human Expertise

Abstract

The growing integration of Artificial Intelligence (AI) in software quality assurance (SQA) is transforming how organizations test, validate, and deliver reliable software systems. This paper explores the evolving paradigm of Human–AI collaboration, emphasizing the need to balance automation efficiency with human expertise. While AI-driven tools enhance accuracy, speed, and defect prediction, human insight remains crucial for contextual interpretation, ethical oversight, and adaptive decision-making. Drawing from recent studies and industry frameworks, this research identifies best practices that optimize hybrid collaboration between humans and intelligent systems. It also examines challenges such as algorithmic bias, explainability, and trust, proposing a co-evolutionary framework for future SQA processes. By harmonizing automation with human creativity and critical reasoning, the study highlights how collaborative intelligence can drive innovation, accountability, and sustained software excellence in the era of intelligent engineering.