Artificial Intelligence (AI) technologies are widely used in computer applications to perform tasks such as monitoring, forecasting, recommending, prediction, and statistical reporting. They are deployed in a variety of systems including driverless vehicles, robot-controlled warehouses, financial forecasting applications, and security enforcement and are increasingly integrated with cloud/fog/edge computing, big data analytics, robotics, Internet-of-Things, mobile computing, smart cities, smart homes, intelligent healthcare, etc. Despite this dramatic progress, the quality assurance of existing AI application development processes is still far from satisfactory and the demand for being able to show demonstrable levels of confidence in such systems is growing. Software testing is a fundamental, effective, and recognized quality assurance method which has shown its cost-effectiveness to ensure the reliability of many complex software systems. However, the adaptation of software testing to the peculiarities of AI applications remains largely unexplored and needs extensive research to be performed. On the other hand, the availability of AI technologies provides an exciting opportunity to improve existing software testing processes, and recent years have shown that machine learning, data mining, knowledge representation, constraint optimization, planning, scheduling, multi-agent systems, etc. have real potential to positively impact on software testing. Recent years have seen a rapid growth of interests in testing AI applications as well as application of AI techniques to software testing. This conference provides an international forum for researchers and practitioners to exchange novel research results, to articulate the problems and challenges from practices, to deepen our understanding of the subject area with new theories, methodologies, techniques, processes models, etc., and to improve the practices with new tools and resources.

IEEE AITest 2026 conference is the eighth edition of a serious IEEE conference with focus on the synergy of AI and software testing. The first IEEE AITest conference took place in April 2019 at New York City, CA, USA. The second and third took place in August 2020 and 2021, respectively, both online due to the COVID-19 pandemic. The fourth took place in August 2022 in the San Francisco Bay Area. While the fifth took place in July 2023 at Athens, Greece; the sixth took place in July 2024 in Shanghai, China; the seventh took place in July in Tucson, USA. This years’ conference is scheduled to take place in Fukuoka, Japan, from 27-30, July 2026. The conference is part of IEEE CISOSE 2026 congress.

Submission Guidelines

All papers must be written in English. Manuscripts must include a title, an abstract, and a list of 4-6 keywords. All papers must be prepared in the IEEE double column proceedings format. Please see: https://www.ieee.org/conferences/publishing/templates.html 

IEEE AITest 2026 uses a double-blind review policy. Authors are required to remove their names, affiliation(s) and other identifying information from the header of the manuscript. Authors are required to cite their previous work in a neutral manner, for example, avoid “in our previous work [3]” and instead use “as shown in [3]”. Papers that do not meet these anonymization requirements may be desk-rejected without further review.

All submitted papers will be peer-reviewed. The name(s) of the author(s) will not be visible to the reviewers of a paper. Authors should report any conflict of interest with the list of PC members during submission of the manuscript, in which case the PC Chairs will exclude the corresponding PC member(s) from reviewing the paper. At least one of the authors of any accepted paper would have to register for the conference and confirm that she/he will present the paper in person.

Authors must submit their manuscripts via the following link by April 1st, 2026, 23:59 AoE at the latest: https://easychair.org/conferences/?conf=ieeeaitest2026. For more information, please visit the conference website at: https://cisose.fit.ac.jp/aitest/. 

List of Topics

• Testing & Verification of AI Systems
  • Verification & Validation Methodologies & Frameworks 
  • Test Oracles & Adequacy Metrics 
  • Deep Learning & Reinforcement Learning Testing 
  • Adversarial Robustness & Safety Assurance 
  • Tools & Resources for Automated Testing of AI
• AI-Driven Software Testing (AI4Test)
  • Autonomous Test Orchestration 
  • Self-Healing & Automated Maintenance 
  • Search-Based & Optimized Testing 
  • Crowdsourced & Swarm Intelligence 
  • Verification of AI-Generated Code
• Assurance of Agentic AI & Large Models
  • Agentic Reliability & Goal-Reasoning 
  • Multi-Agent Coordination & Swarm Validation 
  • LLM Quality, Trust, Fairness, Bias, & Ethics 
  • Multimodal & Generative Model Evaluation 
  • Test of Large Models of Their Applications
• Human-Machine Collaboration
  • Assurance of Human-AI Pair Programming 
  • Human-in-the-Loop (HITL) Verification 
  • Collaborative Debugging & Interactive Testing 
  • Human-AI Interaction (HAI) Metrics for QA 
  • AI Impact on CS Education & Assessment
• Data, Policy, & Observability
  • Data Quality Validation & Enhancement for AI 
  • Quality Assurance for Unstructured Training Data 
  • Synthetic Data Generation & Validation 
  • Large-scale Unstructured Data Quality Certification 
  • AI & Data Management Policies
• Domain-Specific & Physical AI
  • Embodied AI: Robotics, Drones, Autonomous Vehicles 
  • Computer Vision & NLP Quality Assurance 
  • Testing AI for Critical Domain Applications 
  • Cognitive Industry & Smart Manufacturing 
  • Identity & Security Verification

Committees

Organizing committee

• General Chairs
  • Dr. Hong Zhu Oxford Brookes University, UK 
  • Dr. Francesca Lonetti National Research Council, Italy
  • Dr. Yu Song, Fukuoka Institute of Technology, Japan
• Program Chairs
  • Dr. Haihua Chen University of North Texas, USA 
  • Dr. Tatsuhiro Tsuchiya Osaka University, Japan 
  • Dr. Claudio de la Riva University of Oviedo, Spain

Publication

All accepted papers will be published by IEEE Computer Society Press (EI-Index) and included in the IEEE Digital Library. The best papers will be invited to submit an extended version (with at least 30% novel contents) to the selected special issues (TBA). 

Venue

Fukuoka, Japan, from 27-30, July 2026. 

Contact

All questions about submissions should be emailed to Dr. Hong Zhu (hzhu@brookes.ac.uk) and Dr. Haihua Chen (Haihua.Chen@unt.edu)