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

arXiv: https://arxiv.org/abs/2506.14640 · HTML: https://mlsanity.com/paper/2506.14640/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 70 references that are in the map.
- First posted on arXiv 17 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [Getting pwn'd by AI: Penetration Testing with Large Language Models](https://mlsanity.com/paper/2308.00121/) (2023)
- [TOGLL: Correct and Strong Test Oracle Generation with LLMs](https://mlsanity.com/paper/2405.03786/) (2024)
- [AsserT5: Test Assertion Generation Using a Fine-Tuned Code Language Model](https://mlsanity.com/paper/2502.02708/) (2025)
- [A taxonomy of risk-based testing](https://mlsanity.com/paper/1912.11519/) (2019)
- [Acceptance Test Generation with Large Language Models: An Industrial Case Study](https://mlsanity.com/paper/2504.07244/) (2025)
- [An Agent-based Architecture for AI-Enhanced Automated Testing for XR Systems, a Short Paper](https://mlsanity.com/paper/2104.06132/) (2021)
- [Ethical AI-Powered Regression Test Selection](https://mlsanity.com/paper/2106.16050/) (2021)
- [BugBlitz-AI: An Intelligent QA Assistant](https://mlsanity.com/paper/2406.04356/) (2024)
- [On the Effectiveness of LLMs for Manual Test Verifications](https://mlsanity.com/paper/2409.12405/) (2024)
- [WIP: Assessing the Effectiveness of ChatGPT in Preparatory Testing Activities](https://mlsanity.com/paper/2503.03951/) (2025)

## Built on by (most cited first, 2 of 2)

- [gUFO: A Gentle Foundational Ontology for Semantic Web Knowledge Graphs](https://mlsanity.com/paper/2603.20948/) (2026)
- [GUITestScape: Towards Open-set Evaluation on Exploratory GUI Testing](https://mlsanity.com/paper/2605.29532/) (2026)

## Abstract (arXiv)

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.