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AITest 2021 : The IEEE Third International Conference On Artificial Intelligence Testing

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Link: http://www.ieeeaitests.com/
 
When Aug 23, 2021 - Aug 26, 2021
Where Oxford, UK
Submission Deadline May 21, 2021
Notification Due Jun 26, 2021
Final Version Due Jul 10, 2021
Categories    software engineering   software testing   artificial intelligence   deep learning
 

Call For Papers

The IEEE Third International Conference on Artificial Intelligence
Testing (AITest 2021)
23rd-26th August 2021
Virtual Conference Organized by Oxford University, UK
Conference Site: http://ieeeaitests.com
Paper Submission Site: https://easychair.org/conferences/?conf=aitest2021

The IEEE Third International Conference On Artificial Intelligence
Testing (AITest 2021) is an international conference to provide a
platform for researchers, practitioners, and students to present
research results and exchanges ideas on how to test applications
empowered by Artificial Intelligence (AI) and how to empower software
testing methodology and techniques with AI. 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 interest in testing AI applications as well as the
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.


TOPICS OF INTEREST
A. 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
+ 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

B. 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
+ Multi-agent systems for testing and test services
+ Crowdsourcing and swarm intelligence in software testing
+ Genetic algorithms, search-based techniques, and heuristics to the
optimization of testing
+ Knowledge-based and expert systems for software testing

C. Data quality checking for AI applications
+ Quality assurance for unstructured training data
+ Automatic data validation tools
+ Large-scale unstructured data quality certification


TYPES OF CONTRIBUTIONS
A. Regular Papers (8 Pages) And Short Papers (2 Pages)
Regular papers in this track describe original and significant work or
report on case studies and empirical research, and short papers that
describe late-breaking research results or work in progress with
timely and innovative ideas.


B. AI Testing in Practice Papers (8 Pages)
Papers in this track provide a forum for networking, exchanging ideas,
and innovative or experimental practices to address software
engineering research that impacts directly on practice on software
testing for AI.


C. Tool Demo Papers (4 Pages)
The tool demo track provides a forum to present and demonstrate
innovative tools and/or new benchmarking datasets in the context of
software testing for AI.


FORMAT
All papers must be submitted electronically in PDF format using the
IEEE Computer Society Proceedings format (two columns, single-spaced,
10pt font). Papers must not be accepted for publication, or be under
submission to another conference or journal. Each paper will be
reviewed by at least three members of the Program Committee, using a
single-blind reviewing procedure. At least one author of the accepted
paper must register for the conference and confirm that she/he will
present the paper in person. The submission site is AITest 2021 at
EasyChair: https://easychair.org/conferences/?conf=aitest2021


Program Committee Chairs
W.K. Chan, City University of Hong Kong, China
Gordon Fraser, University of Passau, Germany

General Executive Chair
Hong Zhu, Oxford Brookes University, UK

General Chairs
Franz Wotawa, Graz University of Technology, Austria
Jerry Gao, San Jose State University, USA
Marc Roper, University of Strathclyde, UK

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