A-MOST 2021 : Advances in Model-Based Software Testing

  in Conferences   Posted on November 16, 2020

Conference Information

Submission Deadline Wednesday 20 Jan 2021 Proceedings indexed by :
Conference Dates Apr 12, 2021 - Apr 12, 2021
Conference Address Online, Online
Conference & Submission Link https://icst2021.icmc.usp.br/home/a-most-2021
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Call for Papers

The 17th Workshop on Advances in Model Based Testing (A-MOST 2021) co-located with IEEE International Conference on Software Testing, Verification and Validation (ICST 2021)

The increasing complexity, criticality and pervasiveness of software results in new challenges for testing. Model Based Testing (MBT) continues to be an important research area, where new approaches, methods and tools make MBT techniques for automatic test case generation more deployable and useful for industry than ever. Following the success of previous editions, the goal of the A-MOST workshop is to bring researchers and practitioners together to discuss state of the art, practice and future prospects in MBT. Topics and sub-topics (not exhaustive):


Models for component, integration and system testing

Product-line models

(Hybrid) embedded system models

Systems-of-systems models

Architectural models

Models for orchestration and choreography of services

Executable models, simulation and model transformations

Environment and use models

Non-functional models

Models for variant-rich and highly configurable systems

Machine-learning based models


Model-based test generation algorithms

Application of model checking techniques to MBT

Symbolic execution-based techniques

Tracing from requirements models to test models

Performance and predictability of MBT

Test model evolution during the software life-cycle

Risk-based approaches for MBT

Generation of testing infrastructures from models

Combinatorial approaches for MBT

Statistical testing

Non-functional MBT

Derivation of test models by reverse engineering and machine learning


Estimating dependability (e.g., security, safety, reliability) using MBT

Coverage metrics and measurements for structural and (non-)functional models

Cost of testing, economic impact of MBT

Empirical validation, experiences, case studies using MBT


The role of MBT in automata learning (model inference, model mining)

Generating training data for machine learning

Model-based security testing

Statistical model checking

## Submission Format

### Full and Short Papers

Papers should not exceed 8 pages for full papers or 4 pages for short experience and position papers, excluding references – but it is not a strict limit, if you need more space contact the chairs. Each submitted paper must conform to the IEEE two-column publication format. Papers will be reviewed by at least three members from the program committee. Accepted papers will be published in the IEEE Digital Library.

### Journal First

The aim of journal-first papers in category is to further enrich the program of A-MOST, as well as to provide an overall more flexible path to publication and dissemination of original research in model-based testing. The published journal paper must adhere to the following three criteria:

It should be clearly within the scope of the workshop.

It should be recent: it should have been accepted and made publicly available in a journal (online or in print) by 1 January 2019 or more recently.

It has not been presented at, and is not under consideration for, journal-first tracks of other conferences or workshops.

The 2-page submission should provide a concise summary of the published journal paper.

Journal-first submissions must be marked as such in the submission’s title, and must explicitly include full bibliographic details (including a DOI) of the journal publication they are based on. Submissions will be judged on the basis of the above criteria, but also considering how well they would complement the workshop’s technical program.

Submissions have to be sent through the EasyChair web page:



Robert M. Hierons, The University of Sheffield, UK

Manuel Núñez, Universidad Complutense de Madrid, Spain

Alexander Pretschner, Technische Universität München, Germany


Bernhard K. Aichernig, TU Graz, Austria

Anneliese Andrews, University of Denver, US

Paolo Arcaini, National Institute of Informatics, Japan

Kirill Bogdanov, The University of Sheffield, UK

Maxime Cordy, University of Luxembourg, Luxembourg

Xavier Devroey, Delft University of Technology, Netherlands

Khaled El-Fakih, American University of Sharjah, United Arab Emirates

Angelo Gargantini, University of Bergamo, Italy

Florentin Ipate, University of Bucharest, Romania

Zohaib Iqbal, National University of Computer & Emerging Sciences, Pakistan

Yvan Labiche, Carleton University, Canada

Raluca Lefticaru, University of Bradford, UK

Florian Lorber, Aalborg University, Denmark

Mercedes Merayo, Universidad Complutense de Madrid, Spain

Sidney C. Nogueira, Universidade Federal Rural de Pernambuco, Brazil

Ana Paiva, University of Porto, Portugal

Cristina Seceleanu, Mälardalen University, Sweden

Martin Tappler, Schaffhausen Institute of Technology, Switzerland

Uraz Cengiz Turker, University of Leicester, UK

Hüsnü Yenigün, Sabanci University, Turkey


Raluca Lefticaru, University of Bradford, UK

Florian Lorber, Aalborg University, Denmark

Uraz Cengiz Turker, University of Leicester, UK

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