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. In spite of 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IEEE AITest 2023 solicits research papers describing novel and previously unpublished scientific contributions to the field of AT testing. All papers must be original and not simultaneously submitted to another journal or conference.

Two different types of papers can be submitted:
Regular papers (8 pages IEEE double column format) and short papers (2 pages IEEE double column format)
AI Testing In Practice (8 pages IEEE double column format)
Tool Demo Track (4 pages IEEE double column format)

We welcome submissions of both regular research papers (limited to 8 pages), that describe original and significant work or report on case studies and empirical research, and short papers (limited to 2 pages) that describe late-breaking research results or work in progress with timely and innovative ideas. Short papers aim at presenting novel work in progress, novel applications, and novel industry perspectives in the field of AI testing. Each short paper is limited to 2 pages, including tables, figures and references. Short papers will also be peer-reviewed, however, they will be evaluated with a focus on the potential for establishing new ideas and for sparking the interest of participants. Ph.D. students are also invited to submit ongoing work lacking complete results as short papers.

The AI Testing in Practice Track provides a forum for networking, exchanging ideas and innovative or experimental practices to address SE research that impacts directly on practice on software testing for AI.

The tool track provides a forum to present and demonstrate innovative tools and/or new benchmarking datasets in the context of software testing for AI.

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 2023 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. This also includes meta-data in the submitted document as well as acknowledgement sections. 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. The name(s) of the author(s) will be visible in the submission system to the General Chairs and the Program Committee Chairs. Authors should report any conflict of interest with the list of Program Committee members during submission of the manuscript, in which case the Program Committee 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 February 19th, 2023, 23:59 AoE at the latest: https://easychair.org/conferences/?conf=aitest2023

*Committees

**Program Committee :TBC

**Organizing committee

General Chairs:
— Hong Zhu, Oxford Brookes University, UK
— Junhua Ding, University of North Texas, USA

Program Chairs:
— Oum-El-Kheir Aktouf, Grenoble INP - Université Grenoble Alpes, France
— Tao Zhang, Northwestern Polytechnical University, China

Publicity Chairs:
— Moulay El Hassan Charaf, IBN Tofail university, Morocco
— Chuanqi Tao, Chuanqi Tao, Nanjing University of Science and Technology, China
— Nguyen Thanh Binh, The Univ. of Danang, Vietnam

Website Chair:
— HaiHua Chen, University of North Texas, USA

*Contact : All questions about submissions should be emailed to:
- General Inquiries: hzhu@brookes.ac.uk (Hong Zhu), Junhua.ding@unt.edu (Junhua Ding)
- Program Inquiries: oum-el-kheir.aktouf@lcis.grenoble-inp.fr (Oum-El-Kheir Aktouf), tao_zhang@nwpu.edu.cn (Tao Zhang)