Automation and Artificial Intelligence in Quality Assurance

Abstract

The proposed dissertation explores the effects of artificial intelligence (AI) and automation on the productivity of software Quality Assurance (QA) and software quality in contemporary Agile/DevOps and cloud-based delivery settings. This was a mixed-methods design (a survey of 46 practitioners and semi-structured interviews with 10 QA professionals). The results of the survey show that AI-assisted testing is perceived to bring significant advantages in the areas of the regression speed, facilitating continuous testing in CI/CD pipelines, increasing defect detection and test coverage, eliminating the need to invest additional effort in script maintenance, and shortening release times. These findings are supported by interview themes and the key value areas include risk-based test prioritisation, discovery of defect patterns through logs, and self-healing automation. Nevertheless, skills and training gaps, integrations with other tools, cost-related factors, reliance on AI results, and data security standards in controlled environments were also cited by participants as the most significant adoption barriers. Overall, the study concludes that AI improves QA outcomes most effectively when implemented with strong governance and a human-in-the-loop approach.