COSMOS – DevOps for Complex Cyber-physical Systems of Systems
Description
Much of the increasing complexity of ICT systems is being driven by the more distributed and heterogeneous nature of these systems, with Cyber Physical Systems accounting for an increasing portion of Software Ecosystems. This basic premise underpins the COSMOS proposal which focuses on blending best practices DevOps solutions with the development processes used in the CPS context: this will enable the CPS world to deliver software more rapidly and result in more secure and trustworthy systems.COSMOS brings together a balanced consortium of big industry, SMEs and academics which will develop enhanced DevOps pipelines which target development of CPS software. These pipelines will integrate more sophisticated validation and verification (V&V) which will comprise of a mix of static code analysis correlated with issues and bug reports, automated test case generation, runtime verification, Hardware in the Loop (HiL) testing and feedback from field devices. Approaches based on Machine Learning, model based testing and search based test generation will be employed. Techniques to prioritize and schedule testing to maximize efficacy of the testing process and to minimize security threats will also be developed. COSMOS will leverage existing prototype technologies developed by the partners supporting enhancing them throughout the project.The COSMOS CPS pipelines will be validated against 5 use cases provided by industrial partners representing healthcare, avionics, automotive, utility and railway sectors. These will act as reference use cases when promoting the technology amongst Open Source and standardization communities. For the former a specific community building activity will be performed to stimulate engagement with Open Source; for the latter, the standards experience of the coordinator and partners will be employed to promote COSMOS technologies within heavily regulated sectors where there is an increasing need for well-defined software V&V solutions.
Key Data
Projectlead
Dr. Sebastiano Panichella
Project team
Bruno ALMEIDA, Dr. Domenico BIANCULLI, Prof. Lionel BRIAND, Christian Birchler, Davide DE PASQUALE, Prof. Massimiliano DI PENTA, Christopher Drexler, Thomas Graf, James HUNT, Scott Hansen, Pedro MALÓ, David Malgiaritta, Sajad Mazraeh Khatiri, Petr Novobílský, Dr. Annibale Panichella, Dr. Fabrizio Pastore, Oliver Remus, Dr. Carolin Rubner, Prof. Dr. Marcela Ruiz, José António SALVADO NEVES, Prof. Jürgen Spielberger, Dr. Andreas ULRICH, Prof. Andy ZAIDMAN
Project status
completed, 01/2021 - 03/2024
Funding partner
Horizon 2020 / Projekt Nr. 957254
Further documents and links
Publications
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Vulnerabilities introduced by LLMs through code suggestions
2024 Panichella, Sebastiano
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SBFT tool competition 2024 : CPS-UAV test case generation track
2024 Khatiri, Sajad; Saurabh, Prasun; Zimmermann, Timothy; Munasinghe, Charith; Birchler, Christian; Panichella, Sebastiano
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SBFT tool competition 2024 : Python test case generation track
2024 Erni, Nicolas; Al-Ameen, Mohammed; Birchler, Christian; Derakhshanfar, Pouria; Lukasczyk, Stephan
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SensoDat : simulation-based sensor dataset of self-driving cars
2024 Birchler, Christian; Rohrbach, Cyrill; Kehrer, Timo; Panichella, Sebastiano
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How does simulation-based testing for self-driving cars match human perception?
2024 Birchler, Christian; Kombarabettu Mohammed, Tanzil; Rani, Pooja; Nechita, Teodora; Kehrer, Timo; Panichella, Sebastiano
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Simulation-based test case generation for unmanned aerial vehicles in the neighborhood of real flights
2023 Khatiri, Sajad; Panichella, Sebastiano; Tonella, Paolo
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Fuzzing vs SBST : intersections & differences
2023 Guizzo, Giovani; Panichella, Sebastiano
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Machine learning-based test selection for simulation-based testing of self-driving cars software
2023 Birchler, Christian; Khatiri, Sajad; Bosshard, Bill; Gambi, Alessio; Panichella, Sebastiano
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Continuous integration and delivery practices for cyber-physical systems : an interview-based study
2022 Zampetti, Fiorella; Tamburri, Damian A.; Panichella, Sebastiano; Panichella, Annibale; Canfora, Gerardo; Penta, Massimiliano Di
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An empirical characterization of software bugs in open-source Cyber–Physical Systems
2022 Zampetti, Fiorella; Kapur, Ritu; Di Penta, Massimiliano; Panichella, Sebastiano
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NLBSE’22 tool competition
2022 Kallis, Rafael; Chaparro, Oscar; Di Sorbo, Andrea; Panichella, Sebastiano
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Automated identification and qualitative characterization of safety concerns reported in UAV software platforms
2022 Di Sorbo, Andrea; Zampetti, Fiorella; Visaggio, Corrado A.; Di Penta, Massimiliano; Panichella, Sebastiano
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JUGE : an infrastructure for benchmarking Java unit test generators
2022 Devroey, Xavier; Gambi, Alessio; Galeotti, Juan Pablo; Just, René; Kifetew, Fitsum; Panichella, Annibale; Panichella, Sebastiano
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Single and multi-objective test cases prioritization for self-driving cars in virtual environments
2022 Birchler, Christian; Khatiri, Sajad; Derakhshanfar, Pouria; Panichella, Sebastiano; Panichella, Annibale
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Machine learning-based test selection for simulation-based testing of self-driving cars software
2022 Birchler, Christian; Khatiri, Sajad; Bosshard, Bill; Gambi, Alessio; Panichella, Sebastiano
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Using code reviews to automatically configure static analysis tools
2021 Zampetti, Fiorella; Mudbhari, Saghan; Arnaoudova, Venera; Di Penta, Massimiliano; Panichella, Sebastiano; Antoniol, Giuliano
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How to identify class comment types? : a multi-language approach for class comment classification
2021 Rani, Pooja; Panichella, Sebastiano; Leuenberger, Manuel; Di Sorbo, Andrea; Nierstrasz, Oscar
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“Won’t we fix this issue?” : qualitative characterization and automated identification of wontfix issues on GitHub
2021 Panichella, Sebastiano; Canfora, Gerardo; Di Sorbo, Andrea
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Exposed! : a case study on the vulnerability-proneness of Google Play Apps
2021 Di Sorbo, Andrea; Panichella, Sebastiano
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Predicting issue types on GitHub
2020 Kallis, Rafael; Di Sorbo, Andrea; Canfora, Gerardo; Panichella, Sebastiano