Akshay Singh and Omar Al-Azzam, Saint Cloud State University (SCSU), USA

The study investigates the background, advantages, and difficulties of AI-based testing. The use of artificial intelligence (AI) has shown great promise as a means of enhancing software testing procedures. To improve test case generation, bug prediction, and test result analysis, AI-based testing approaches use machine learning, NLP (natural language Processing), GUIs (graphical user interfaces), genetic algorithms, and robotic process automation. We also provide a brief literature review of recent studies in the field, focusing on the various approaches and tools proposed for AI-based software testing. We conclude with a strategy for introducing AI-based testing and a list of possible approaches and resources. Overall, this paper provides a comprehensive survey of AI-based software testing and highlights the potential benefits and challenges of this emerging field.

Artificial intelligence, Software testing, Machine learning, Natural language processing, Graphical user interfaces, Computer vision, genetic algorithms, Robotics process automation, Tools, Trends.