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Learning-based testing for autonomous systems using spatial and temporal requirements

Published: 03 September 2018 Publication History

Abstract

Cooperating cyber-physical systems-of-systems (CO-CPS) such as vehicle platoons, robot teams or drone swarms usually have strict safety requirements on both spatial and temporal behavior. Learning-based testing is a combination of machine learning and model checking that has been successfully used for black-box requirements testing of cyber-physical systems-of-systems. We present an overview of research in progress to apply learning-based testing to evaluate spatio-temporal requirements on autonomous systems-of-systems through modeling and simulation.

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cover image ACM Conferences
MASES 2018: Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis
September 2018
52 pages
ISBN:9781450359726
DOI:10.1145/3243127
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 September 2018

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Author Tags

  1. Automotive software
  2. black-box testing
  3. learning-based testing
  4. machine learning
  5. model-based testing
  6. requirements testing
  7. spatio-temporal logic

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  • (2023)Testing, Validation, and Verification of Robotic and Autonomous Systems: A Systematic ReviewACM Transactions on Software Engineering and Methodology10.1145/354294532:2(1-61)Online publication date: 30-Mar-2023
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