Navigating the growing field of research on AI for software testing -- the taxonomy for AI-augmented software testing and an ontology-driven literature survey

TL;DR

This paper reviews recent advances in AI-augmented software testing, introduces a taxonomy for classifying AI-driven testing methods, and identifies open research questions in the field.

Contribution

It presents a comprehensive taxonomy for AI-augmented software testing and conducts an ontology-driven literature survey to categorize recent research and highlight future directions.

Findings

AI enhances both manual and automated testing methods.

The taxonomy classifies various levels of AI integration in testing.

Identifies key open research challenges in AI for 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.