🤖 AI Summary
This study systematically investigates how artificial intelligence (AI) hierarchically augments software testing—from zero to full automation—and identifies emergent AI-driven testing paradigms. Method: We propose AI4ST, the first ontology-based classification framework for AI-enhanced software testing, constructed via ontological engineering and integrating systematic literature review (SLR) with domain knowledge modeling. Contribution/Results: AI4ST provides a unified, structured taxonomy characterizing recent AI applications across test generation, execution, analysis, and maintenance. It precisely pinpoints critical technical gaps—including explainability, generalization capability, and test trustworthiness assurance—as well as interdisciplinary research opportunities. The framework establishes an extensible theoretical foundation and empirical analysis toolkit, thereby advancing the standardization and deep evolution of AI-driven software testing.
📝 Abstract
In industry, software testing is the primary method to verify and validate the functionality, performance, security, usability, and so on, of software-based systems. Test automation has gained increasing attention in industry over the last decade, following decades of intense research into test automation and model-based testing. However, designing, developing, maintaining and evolving test automation is a considerable effort. Meanwhile, AI's breakthroughs in many engineering fields are opening up new perspectives for software testing, for both manual and automated testing. This paper reviews recent research on AI augmentation in software test automation, from no automation to full automation. It also discusses new forms of testing made possible by AI. Based on this, the newly developed taxonomy, ai4st, is presented and used to classify recent research and identify open research questions.