AITest 2023: The 5th IEEE International Conference on Artificial Intelligence Testing Athens, Greece, July 17-20, 2023 |
Conference website | https://ieeeaitest.com/ |
Submission link | https://easychair.org/conferences/?conf=aitest2023 |
Abstract registration deadline | February 12, 2023 |
Submission deadline | February 19, 2023 |
Author notification | April 24, 2023 |
Final paper submission (camera-ready) and conference registration | May 15, 2023 |
Submission Guidelines
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
Preprints
Authors who have submitted articles for publication by the IEEE may be interested in posting preprint versions of the same article. In order to find out more about the allowed forms of preprints and to understand what counts as a prior publication, please see IEEE’s Sharing and Posting Policies as well as Section 8.1.9 on “Electronic Information Dissemination” in the IEEE Publication Services and Products Board Operations Manual.
Paper publication
All accepted papers will be published by IEEE Computer Society Press (EI-Index) and included in the IEEE Digital Library. For publication, each accepted paper is required to be registered by one of its authors, and at least one author is required to attend and present the paper at the conference for the paper to be included in the final technical program and the IEEE Digital Library.
Special issues
The authors of the best papers will be invited to also submit their work (with at least 30% novel contents) to the following special issues:
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Computer Standards & Interfaces
- TBA
List of Topics
The conference invites papers of original research on AI testing and reports of the best practices in the industry as well as the challenges in practice and research. Topics of interest include (but are not limited to) the following:
- Testing AI applications
- Methodologies for testing, verification and validation of AI applications
- Process models for testing AI applications and quality assurance activities and procedures
- Quality models of AI applications and quality attributes of AI applications, such as correctness, reliability, safety, security, accuracy, precision, comprehensibility, explainability, etc.
- Whole lifecycle of AI applications, including analysis, design, development, deployment, operation and evolution
- Quality evaluation and validation of the datasets that are used for building the AI applications
- Techniques for testing AI applications
- Test case design, test data generation, test prioritization, test reduction, etc.
- Metrics and measurements of the adequacy of testing AI applications
- Test oracle for checking the correctness of AI application on test cases
- Tools and environment for automated and semi-automated software testing AI applications for various testing activities and management of testing resources
- Specific concerns of software testing with various specific types of AI technologies and AI applications
- Applications of AI techniques to software testing
- Machine learning applications to software testing, such as test case generation, test effectiveness prediction and optimization, test adequacy improvement, test cost reduction, etc.
- Constraint Programming for test case generation and test suite reduction
- Constraint Scheduling and Optimization for test case prioritization and test execution scheduling
- Crowdsourcing and swarm intelligence in software testing
- Genetic algorithms, search-based techniques and heuristics to optimization of testing
- Data quality evaluation for AI applications
- Automatic data validation tools
- Quality assurance for unstructured training data
- Large-scale unstructured data quality certification
- Techniques for testing deep neural network learning, reinforcement learning and graph learning
Committees
Program Committee
- TBC
Organizing committee
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General Chairs:— Hong Zhu, Oxford Brookes University, UK— Junhua Ding, University of North Texas, USAProgram Chairs:— Oum-El-Kheir Aktouf, Grenoble INP - Université Grenoble Alpes, France— Tao Zhang, Northwestern Polytechnical University, ChinaPublicity 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, VietnamWebsite 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)