SOFL+MSVL 2022

Call for Papers

Background and Objectives:

There is a growing interest in applying formal methods in practice to improve software reliability and security, but only with a few exceptions, this interest has not been successfully converted into the reality. How to enable practitioners to use formal techniques easily and effectively still remains challenging. The Structured Object-Oriented Formal Language (SOFL) has been developed to address this challenge by providing a comprehensible specification language, a practical modeling method, various verification and validation techniques, and tool support through effective integration of formal methods with conventional software engineering techniques. The Modeling, Simulation and Verification Language (MSVL) is a parallel programming language, and its supporting toolkit MSV has been developed to enable us to model, simulate and verify a system in a formal manner.
This workshop aims to continuously promote the development and combinations of the SOFL formal engineering method and the formal method MSVL, as well as the applications of their fundamental principles or specific techniques to developing other formal engineering techniques. We expect to bring industrial, academic and government experts of SOFL and MSVL to communicate and to exchange ideas. Researchers, practitioners, tool developers and users, and technology transfer experts are all welcome. The scope of the interest includes, but not limited to, all the possible issues in relation to SOFL, MSVL, or their applications in both developing other formal engineering techniques and specific software systems.

Scope and Topics:

Modeling and Specification
Integration of prototyping and formal specification
Integration of Agile methods and formal specification
Specification inspection and verification
Specification animation
Automatic transformation
Specification-based inspection and verification
Specification-based testing
Evolution and refinement
Model checking
Software process
Project management
Service-oriented computing
Data intensive computing
Many core parallel computing
Security of software
Application
Semantics
Software Tools

Submission and Publication:

Submissions to the workshop can be done through the EasyChair system https://easychair.org/conferences/?conf=soflmsvl2022 and must not have been published or be concurrently considered for publication elsewhere. Papers must be written in English and should be no longer than 20 pages in Springer's LNCS format. The workshop proceedings will be published as an LNCS post-proceedings by Springer.

Organizing Committee

Program Chairs:

• Shaoying Liu, Hiroshima University, Japan
• Zhenhua Duan, Xidian University, China
  
• Ai Liu, Hiroshima University, Japan

Program Committee:

Yuting Chen, Shanghai Jiaotong University, China
Busalire Emeka, Hosei University, Japan
Kazuhiro Ogata, JAIST, Japan
Xinfeng Shu, Xi'an University of Posts and Telecomunications, China
Rong Wang, Galileo Co. Ltd., Japan
Xi Wang, Shanghai University, China
Xiaobing Wang, Xidian University, China
Bin Yu, Xidian University, China
Zhen You, Jiangxi Normal University, China

Program

Keynote talk:

• Title: Risks Management around Machine Learning Software
• Speaker: Shin Nakajima is a Professor-Emeritus at National Institute of Informatics (NII), a Visiting Professor at The Open University of Japan (OUJ), and a Visiting Researcher at The National Institute of Advanced Industrial Science and Technology (AIST). He has worked in the area of Software Engineering, Formal Methods, Automated Verification, and Software Innovation.
• Abstract: As widely recognized, quality issues of machine learning software become a major concern, because such software systems are embedded in social infrastructure, which motivates research activities to apply Software Engineering practices to the machine learning software development. In early days, the test oracle problem was addressed in view of testing baseline functionalities such as model accuracy or model robustness; metamorphic testing with carefully generated fuzz is found useful. Later, the ethical aspects, fairness and privacy, are considered mandatory, and the quality assurance activities put focus more on data modeling with posteriori testing of fairness or privacy. Those quality characteristics are more or less inconsistent and thus need a thorough study of the trade-off between them. To work on the issues, a multi-layered quality model is introduced together with both technical and non-technical ways for the quality management. All these mitigate risks that machine learning software may bring about. Moreover, machine learning software is subject to laws or regulations, which may incur risks affecting the machine learning innovation. The risk management of machine learning software must consider all these aspects, from the baseline functionalities to the ethical and lawful aspects. This talk sketches recent activities on the quality assurance of machine learning software, primarily viewed from the AIQM (Artificial Intelligence Quality Management), a joint industry-academia-government project in Japan, and some of the legal issues with regard to European GDPR and AI-ACT, or domestic IPR in Japan.

Workshop program:

Download the program here

Participation

Use the following Zoom URL to join the workshop program:

https://zoom.us/j/98732317437?pwd=TkNsb1g0ZGFRaWUyMXFPQVJzRms2QT09