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)
Kontaktperson : Sebastiano Panichella

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

Birchler, Christian; Khatiri, Sajad; Bosshard, Bill; Gambi, Alessio; Panichella, Sebastiano,

2023.

Machine learning-based test selection for simulation-based testing of self-driving cars software.

Empirical Software Engineering.

28(71).

Verfügbar unter: https://doi.org/10.1007/s10664-023-10286-y
Rani, Pooja; Blasi, Arianna; Stulova, Nataliia; Panichella, Sebastiano; Gorla, Alessandra; Nierstrasz, Oscar,

2023.

A decade of code comment quality assessment : a systematic literature review.

Journal of Systems and Software.

195(111515).

Verfügbar unter: https://doi.org/10.1016/j.jss.2022.111515
Birchler, Christian; Ganz, Nicolas; Khatiri, Sajad; Gambi, Alessio; Panichella, Sebastiano,

2023.

Cost-effective simulation-based test selection in self-driving cars software.

Science of Computer Programming.

226(102926).

Verfügbar unter: https://doi.org/10.1016/j.scico.2023.102926
Guizzo, Giovani; Panichella, Sebastiano,

2023.

Fuzzing vs SBST : intersections & differences [Paper].

In:

15th International Workshop on Search-Based Software Testing (SBST), Pittsburgh, USA, 9 May 2022.

Association for Computing Machinery.

S. 105-107.

Verfügbar unter: https://doi.org/10.1145/3573074.3573102
Khatiri, Sajad; Panichella, Sebastiano; Tonella, Paolo,

2023.

Simulation-based test case generation for unmanned aerial vehicles in the neighborhood of real flights [Paper].

In:

16th IEEE International Conference on Software Testing, Verification and Validation (ICST), Dublin, Ireland, 16-20 April 2023.

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

Verfügbar unter: https://doi.org/10.21256/zhaw-26597
Di Sorbo, Andrea; Panichella, Sebastiano,

2023.

Summary of the 1st Natural Language-based Software Engineering Workshop (NLBSE 2022) [Paper].

In:

1st International Workshop on Natural Language-Based Software Engineering (NLBSE), Pittsburgh, USA (online), 8 May 2022.

Association for Computing Machinery.

S. 101-104.

Verfügbar unter: https://doi.org/10.1145/3573074.3573101
Birchler, Christian; Khatiri, Sajad; Bosshard, Bill; Gambi, Alessio; Panichella, Sebastiano,

2022.

Machine learning-based test selection for simulation-based testing of self-driving cars software.

arXiv.

Verfügbar unter: https://doi.org/10.48550/arXiv.2212.04769
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.

Automated identification and qualitative characterization of safety concerns reported in UAV software platforms.

ACM Transactions on Software Engineering and Methodology.

Verfügbar unter: https://doi.org/10.1145/3564821
Zampetti, Fiorella; Tamburri, Damian A.; Panichella, Sebastiano; Panichella, Annibale; Canfora, Gerardo; Penta, Massimiliano Di,

2022.

Continuous integration and delivery practices for cyber-physical systems : an interview-based study.

ACM Transactions on Software Engineering and Methodology.

Verfügbar unter: https://doi.org/10.1145/3571854
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
Devroey, Xavier; Gambi, Alessio; Galeotti, Juan Pablo; Just, René; Kifetew, Fitsum; Panichella, Annibale; Panichella, Sebastiano,

2022.

JUGE : an infrastructure for benchmarking Java unit test generators.

Software Testing, Verification and Reliability.

S. e1838.

Verfügbar unter: https://doi.org/10.1002/stvr.1838
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/10.1145/3528588.3528664
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.

“Won’t we fix this issue?” : qualitative characterization and automated identification of wontfix issues on GitHub.

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

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