Published Paper Details:

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has significantly transformed the landscape of software automation. This paper presents a comprehensive review of the current state and emerging trends in leveraging AI and ML techniques to optimize software automation processes while integrating robust security features. We explore how intelligent algorithms contribute to enhanced testing, code generation, deployment, and monitoring, thus reducing human effort and errors. Furthermore, we delve into the role of ML models in proactively identifying security threats, predicting vulnerabilities, and automating responses to cyberattacks in real-time. This review also highlights key frameworks, tools, and case studies that demonstrate successful implementation in industrial and enterprise-level environments. Finally, we identify challenges and propose future directions for integrating secure, intelligent automation in software development lifecycles. Also, in this paper our aim is to conduct the software testing with AI for fulfilling customer's application and to create related applications with latest AI tools.