COSMOS

DevOps for Complex Cyber-physical Systems

Full project details (EU Research results portal): https://cordis.europa.eu/project/id/957254

Project 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.

EuroVoc IDs: /natural sciences/computer and information sciences/software

EU Programme: Horizon 2020

EU Project

Project publications:

EU ProjectHas TitleHas CategoryHas TypeHas YearHas DOI
COSMOSCI/CD Pipelines Evolution and Restructuring: A Qualitative and Quantitative StudySoftware Engineering, Testing, and DevOpsConference proceedings2021https://doi.org/10.1109/icsme52107.2021.00048
COSMOSNLBSE'22 tool competitionSoftware Engineering, Testing, and DevOpsConference proceedings2022https://doi.org/10.1145/3528588.3528664
COSMOSAn Empirical Investigation of Relevant Changes and Automation Needs in Modern Code ReviewSoftware Engineering, Testing, and DevOpsConference proceedings2021https://doi.org/10.1007/s10664-020-09870-3
COSMOSSBFT Tool Competition 2024 - Python Test Case Generation TrackSoftware Engineering, Testing, and DevOpsConference proceedings2024https://doi.org/10.5281/zenodo.10554259
COSMOSHybrid Multi-level Crossover for Unit Test Case GenerationSoftware Engineering, Testing, and DevOpsConference proceedings2021https://doi.org/10.48550/arxiv.2108.05466
COSMOSSBST Tool Competition 2021Software Engineering, Testing, and DevOpsConference proceedings2021https://doi.org/10.1109/sbst52555.2021.00011
COSMOSSingle and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual EnvironmentsSoftware Engineering, Testing, and DevOpsPeer reviewed articles2022https://doi.org/10.1145/3533818
COSMOSA decade of code comment quality assessment: A systematic literature reviewSoftware Engineering, Testing, and DevOpsPeer reviewed articles2023https://doi.org/10.1016/j.jss.2022.111515
COSMOSMetamorphic Testing for Web System SecuritySoftware Engineering, Testing, and DevOpsPeer reviewed articles2023https://doi.org/10.1109/tse.2023.3256322
COSMOSBasic block coverage for search-based unit testing and crash reproductionSoftware Engineering, Testing, and DevOpsPeer reviewed articles2022https://doi.org/10.1007/s10664-022-10155-0
COSMOSToward Automatically Completing GitHub WorkflowsSoftware Engineering, Testing, and DevOpsConference proceedings2024https://doi.org/10.1145/3597503.3623351
COSMOSJUGE: An Infrastructure for Benchmarking Java Unit Test GeneratorsSoftware Engineering, Testing, and DevOpsPeer reviewed articles2022https://doi.org/10.48550/arxiv.2106.07520
COSMOSDiversity-guided Search Exploration for Self-driving Cars Test Generation through Frenet Space EncodingSoftware Engineering, Testing, and DevOpsConference proceedings2024https://doi.org/10.48550/arxiv.2401.14682
COSMOSTowards Log SlicingSoftware Engineering, Testing, and DevOpsConference proceedings2023https://doi.org/10.1007/978-3-031-30826-0 14
COSMOSAutomated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software PlatformsSoftware Engineering, Testing, and DevOpsPeer reviewed articles2023https://doi.org/10.1145/3564821
COSMOSGenerating Class-Level Integration Tests Using Call Site InformationSoftware Engineering, Testing, and DevOpsPeer reviewed articles2022https://doi.org/10.1109/tse.2022.3209625
COSMOSCost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-ScissorSoftware Engineering, Testing, and DevOpsConference proceedings2022https://doi.org/10.1109/saner53432.2022.00030
COSMOSWon t We Fix this Issue? Qualitative characterization and automated identification of wontfix issues on GitHubSoftware Engineering, Testing, and DevOpsPeer reviewed articles2021https://doi.org/10.1016/j.infsof.2021.106665
COSMOSPredicting issue types on GitHubSoftware Engineering, Testing, and DevOpsConference proceedings2021https://doi.org/10.48550/arxiv.2107.09936
COSMOSMany-Objective Reinforcement Learning for Online Testing of DNN-Enabled SystemsSoftware Engineering, Testing, and DevOpsPeer reviewed articles2023https://doi.org/10.1109/icse48619.2023.00155
COSMOSSystematic Evaluation of Deep Learning Models for Failure PredictionSoftware Engineering, Testing, and DevOpsPeer reviewed articles2024https://doi.org/10.48550/arxiv.2303.07230