Christopher Brix, Mark Niklas Müller, Stanley Bak, Taylor T. Johnson, Changliu Liu
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引用次数: 16
Abstract
Abstract This paper presents a summary and meta-analysis of the first three iterations of the annual International Verification of Neural Networks Competition (VNN-COMP), held in 2020, 2021, and 2022. In the VNN-COMP, participants submit software tools that analyze whether given neural networks satisfy specifications describing their input-output behavior. These neural networks and specifications cover a variety of problem classes and tasks, corresponding to safety and robustness properties in image classification, neural control, reinforcement learning, and autonomous systems. We summarize the key processes, rules, and results, present trends observed over the last three years, and provide an outlook into possible future developments.
期刊介绍：
The International Journal on Software Tools for Technology Transfer (STTT) provides a forum for the discussion of all aspects of tools supporting the development of computer systems. It offers, above all, a tool-oriented link between academic research and industrial practice.
Tool support for the development of reliable and correct computer-based systems is of growing importance, and a wealth of design methodologies, algorithms, and associated tools have been developed in different areas of computer science. However, each area has its own culture and terminology, preventing researchers from taking advantage of the results obtained by colleagues in other fields. Tool builders are often unaware of the work done by others, and thus unable to apply it. The situation is even more critical when considering the transfer of new technology into industrial practice.
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