COSMOS – DevOps for Complex Cyber-physical Systems of Systems
Auf einen Blick
- Projektleiter/in : Dr. Sebastiano Panichella
- Projektteam : 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, Dr. Marcela Ruiz, José António SALVADO NEVES, Prof. Jürgen Spielberger, Dr. Andreas ULRICH, Prof. Andy ZAIDMAN
- Projektstatus : laufend
- Drittmittelgeber : EU und andere Internationale Programme (Horizon 2020 / Projekt Nr. 957254)
Beschreibung
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.
Publikationen
-
Zampetti, Fiorella; Kapur, Ritu; Di Penta, Massimiliano; Panichella, Sebastiano,
2022.
An empirical characterization of software bugs in open-source Cyber–Physical Systems.
Journal of Systems and Software.
192(111425).
Verfügbar unter: https://doi.org/10.1016/j.jss.2022.111425
-
Di Sorbo, Andrea; Zampetti, Fiorella; Visaggio, Corrado A.; Di Penta, Massimiliano; Panichella, Sebastiano,
2022.
ACM Transactions on Software Engineering and Methodology.
Verfügbar unter: https://doi.org/10.1145/3564821
-
Birchler, Christian; Ganz, Nicolas; Khatiri, Sajad; Gambi, Alessio; Panichella, Sebastiano,
2022.
Cost-effective simulation-based test selection in self-driving cars software with SDC-Scissor [Paper].
In:
2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER).
29th IEEE International Conference on Software Analysis, Evolution, and Reengineering, Honolulu, USA (online), 15-18 March 2022.
IEEE.
S. 164-168.
Verfügbar unter: https://doi.org/10.1109/SANER53432.2022.00030
-
Kallis, Rafael; Chaparro, Oscar; Di Sorbo, Andrea; Panichella, Sebastiano,
2022.
NLBSE’22 tool competition [Paper].
In:
2022 IEEE/ACM 1st International Workshop on Natural Language-Based Software Engineering (NLBSE).
1st International Workshop on Natural Language-Based Software Engineering (NLBSE), Pittsburgh, USA (online), 8 May 2022.
IEEE.
S. 25-28.
Verfügbar unter: https://doi.org/10.21256/zhaw-25771
-
Birchler, Christian; Khatiri, Sajad; Derakhshanfar, Pouria; Panichella, Sebastiano; Panichella, Annibale,
2022.
Single and multi-objective test cases prioritization for self-driving cars in virtual environments.
ACM Transactions on Software Engineering and Methodology.
37(4), S. 111.
Verfügbar unter: https://doi.org/10.1145/3533818
-
Panichella, Annibale; Panichella, Sebastiano; Fraser, Gordon; Sawant, Anand; Hellendoorn, Vincent,
2022.
Test smells 20 years later : detectability, validity, and reliability.
Empirical Software Engineering.
27(7), S. 170.
Verfügbar unter: https://doi.org/10.1007/s10664-022-10207-5
-
Rani, Pooja; Panichella, Sebastiano; Leuenberger, Manuel; Di Sorbo, Andrea; Nierstrasz, Oscar,
2021.
How to identify class comment types? : a multi-language approach for class comment classification.
Journal of Systems and Software.
181(111047).
Verfügbar unter: https://doi.org/10.1016/j.jss.2021.111047
-
Di Sorbo, Andrea; Visaggio, Corrado A.; Di Penta, Massimiliano; Canfora, Gerardo; Panichella, Sebastiano,
2021.
An NLP-based tool for software artifacts analysis [Paper].
In:
37th International Conference on Software Maintenance and Evolution (ICSME), Luxembourg, 27 September - 1 October 2021.
Winterthur:
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Verfügbar unter: https://doi.org/10.21256/zhaw-23363
-
Panichella, Sebastiano; Gambi, Alessio; Zampetti, Fiorella; Riccio, Vincenzo,
2021.
SBST tool competition 2021 [Paper].
In:
2021 IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST).
14th International Workshop on Search-Based Software Testing (SBST), Madrid, Spain, 31 May 2021.
IEEE.
S. 20-27.
Verfügbar unter: https://doi.org/10.1109/SBST52555.2021.00011
-
Zampetti, Fiorella; Mudbhari, Saghan; Arnaoudova, Venera; Di Penta, Massimiliano; Panichella, Sebastiano; Antoniol, Giuliano,
2021.
Using code reviews to automatically configure static analysis tools.
Empirical Software Engineering.
27(1), S. 28.
Verfügbar unter: https://doi.org/10.1007/s10664-021-10076-4
-
Panichella, Sebastiano; Canfora, Gerardo; Di Sorbo, Andrea,
2021.
Information and Software Technology.
139(106665).
Verfügbar unter: https://doi.org/10.1016/j.infsof.2021.106665
-
Roy, Devjeet; Zhang, Ziyi; Ma, Maggie; Arnaoudova, Venera; Panichella, Annibale; Panichella, Sebastiano; Gonzalez, Danielle; Mirakhorli, Mehdi,
2020.
DeepTC-Enhancer : improving the readability of automatically generated tests [Paper].
In:
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering.
35th IEEE/ACM International Conference on Automated Software Engineering (ASE), Virtual Event, 21-25 September 2020.
ACM.
S. 287-298.
Verfügbar unter: https://doi.org/10.1145/3324884.3416622
-
Kallis, Rafael; Di Sorbo, Andrea; Canfora, Gerardo; Panichella, Sebastiano,
2020.
Predicting issue types on GitHub.
Science of Computer Programming.
205(102598).
Verfügbar unter: https://doi.org/10.1016/j.scico.2020.102598