DevOps for Complex Cyber-physical Systems

Leistungen

Automated bad practice resolution recommender for CPS

The deliverable includes the description and prototypal implementation of an approach and tool for the automated resolution of bad practices in a CI/CD pipeline for CPS. Once the bad practice has been detected, this tool leverages the body of knowledge collected in D3.3 as well as historical data to recommend pipeline repairs.

Prototype of a toolset enabling balancing of co-simulation and physical testing

The toolset integrates solutions to balance testing based on simulation and testing in physical environments. It relies on machine learning to evaluate and improve the quality of simulation predictions and minimize physical testing.

Build schedule tool prototype

The deliverable provides a tool that determines which type of test to use at build stage depending on the changes applied to both test and production code. The build will consider both manually-written tests or those generated in D5.3.

Prototype of a trace diagnostics toolset for checking signal-based properties of CPS

The toolset will extend run-time verification techniques to provide informative feedback to the user in case run-time verification yields a negative verdict.

Prototype of a toolset supporting automated testing in co-simulated environments

The toolset will leverage model-based techniques, meta-heuristic search, and machine learning to drive testing in co-simulated environments. It will integrate meta-heuristic search and machine learning to select test inputs based on specification models.

Prototype of evolutionary toolsets enhanced by machine learning to support security vulnerability testing

The toolset will automatically identify inputs triggering vulnerabilities. It will rely on machine learning to process software data such as source code and change information.

Automated bad practice detectors for CPS DevOps pipelines

The deliverable includes the description and prototypal implementation of an approach and tool for the automated identification of bad practices in DevOps CI/CD pipelines targeting CPS. The detector will feature antipatterns elicited in D3.2 for which the automated detection is deemed feasible and appropriate.

AI-based Prototypes supporting CPS selfadaptability

COSMOS aims to increase CPS Self-adaptability to Diverse Contexts. Thus, D6.4 aims at delivering a framework integrating a set of metrics and tools, which are adequate for improving CPS self-adaptability to unprecedented contextual scenarios.

Prototype of a toolset supporting automated testing in physical environments

The toolset will leverage machine learning to enable automated testing in physical environments. Machine learning will enable the definition of strategies for the generation of safe test inputs, the identification of solutions for the prediction of execution results, and the automated identification of failures.

Prototypes for the Quality Assessment and Monitoring of CPS in the Field

To better monitor and assess CPS development and evolution, COSMOS aims to integrate DevOps tools based on CPS specific test mutations and coverage criteria, code/test smells (partially investigated in WP 5), anti-patterns (investigated also in WP 3), and vulnerabilities that can affect/concern HIL activities. Thus, D6.3 aims at delivering a framework integrating a set of metrics and tools, which are more adequate for CPS monitoring, as they enable a better CPS quality assessment as well as the detection and self-recovery of different forms of CPS degradation.

Tools enabling the Two-speed DevOps cycle for CPS

COSMOS extends traditional DevOps pipelines developing AI-based solutions to support short and expensive DevOps cycles for CPS. Thus, D6.2 aims at delivering a prioritization framework designed and implemented to contribute to the reduction of testing costs of CPS. This framework leverages a set of metrics and tools integrated into CPS DevOps pipelines, that allow to select/prioritize the CPS changes that should be verified within the fast and/or the slow cycle, focusing on the one that can lead to failures and unexpected behaviors.

Test generation tool prototype

The deliverable consists of a test case generation tool that synthetizes unit-level tests from high-level, expensive tests. The tool will rely on meta-heuristics, static and dynamic seeding, and test carving techniques.

Prototype handling CPS Change & Behavioral models in CI/CD pipelines for CPS

COSMOS extends traditional DevOps pipelines to cater for specific failures occurring in CPS, this for supporting their automated detection, prediction, and fixing. Thus, D6.1 aims at delivery of tools for the automated extraction of change and behavioral models, as well as tools leveraging these models to enable the CPS behavior/failure monitoring, prediction, and self-healing.

Prototype of a toolset for run-time verification of CPS

The toolset will provide run-time verification techniques for checking signal-based temporal properties on CPS execution traces both in offline and in online settings.

Complete framework of test generation and build schedule tooling

The deliverable extends and completes D5.3 and D5.4 in two ways. First, it will include additional testing strategies for CPS: (1) test oracle enhancer, (2) test case generation based on User Feedbacks. Second, this delivery will update the build schedule based on the new type of tests generated in this deliverable.

Complete framework, refining quality assessment and self-adaptability solutions

COSMOS's initial frameworks and tools are refined and evaluated, over the course of the project, by considering the COSMOS's case studies; these activities will be performed in an incremental, continuous manner, to help to consolidate and further assess the implemented quality assessment and self-adaptability solutions.

Prototype of a toolset for specification-based functional security testing of CPS

The toolset will rely on high-level specifications of attacks and security properties to automate security testing.

Prototype of a toolset for code analysis of CPS

The toolset will include analysis techniques to enable security audit of CPS code bases.

Prototype of a toolset for machine learning-enabled detection of vulnerabilities in CPS

The toolset will rely on machine learning to identify inputs triggering anomalous execution flows that characterize vulnerabilities. The analysis will be based on data collected at development time and in the field.

Prototype tool for the smart allocation of jobs on HiL and simulators, and build prioritiziation

The deliverable includes the description and prototypal implementation of a plugin for CI/CD servers (e.g., available for Jenkins or other popular pieces of technology) capable of exploiting the (limited) availability of simulators and HiL, and consequently allocate CI/CD jobs on them, by also prioritizing incoming builds based on their predicted execution time and on other features.

Project Website

A public website describing the project objectives approach partners involved and expected results in the initial version Later updates will include access to public deliverables downloadable papers and articles and other information about the project The website will be updated periodically

Approach for the smart allocation of jobs on HiL and simulators, and build prioritisation

The report describes an approach for combining the allocation of simulators and hardware with test prioritization and statistical techniques in support of smart allocation of test jobs on simulators and physical devices

Handbook of refactoring of production code + prototype refactorings

The report provides an overview of refactoring operations to address antipatterns in DevOps pipelines The outcome will be a handbook of refactoring and a prototype of a recommending system for refactoring opportunities

Framework of metrics for production code anti-patterns for DevOps

The report collects source code metrics that are related to antipatterns in DevOps pipelines These metrics will be determined via software repository mining techniques and statistical analyses

Press and Media Materials

A Press Release and other supporting materials for creating awareness of the first prototype technologies availability and initial industrial evaluations.

Methodology for setting-up CI/CD pipelines for CPS

The report provides methodological insights on how to configure a pipeline for CPS, leveraging the approaches and tools developed in WP4-WP6. That is, this WP constitutes the methodology necessary to frame and properly apply COSMOS approaches and tools onto CPS development projects.

Integrated Platform - Interim Version

This deliverable will comprise a software prototype and a report The prototype will synthesise relevant technical contributions from work packages 36 into an integrated platform The report will outline the architecture of the platform and the extension points that tool developers can leverage to integrate additional technologies with the platform

Catalogue of good and bad practices of DevOps for CPS

The report provides a catalogue of patterns and antipatterns related to the application of DevOps in CPS developments Such patterns and antipatterns will be elicited through an analysis using groundedtheory methodologies of data collected in D31 as well as by mining information from software repositories

Project Presentation and Brochure

Materials to present the project to interested parties including details on the technical challenges and the approaches being developed within the project to address the challenges the expected impact from both a technical and societal standpoint and where to obtain further information and details concerning the project research and development work

Evaluation Methodology

This deliverable builds upon the initial Evaluation Plan from workpackage 1 to provide a detailed specification of the evaluation methodologies and measurements that will be carried out for each of the four Use Cases It will include the scope of validation activities the metrics KPIs to be evaluated comprising targets such as productivity efficiency economical benefits and the associated test cases and methods to gather and evaluate these measures

Report on a high-level specification language for signal-based properties of CPS

The report describes a domainspecific language for expressing typical patterns of signalbased temporal properties eg spike and oscillatory behaviors

Integrated Platform - Final Version

This deliverable will provide the final version of the integrated platform (an interim version of which was delivered in D7.1) that will now include the final versions of all the tools developed in the project.

Veröffentlichungen

Mobile Cyber Gateway Security Control

Autoren: Jan Prochazka,Petr Novobilsky, Dana Prochazkova
Veröffentlicht in: Proceedings of the 31st European Safety and Reliability Conference, 2021
Herausgeber: Research Publishing

Basic block coverage for unit test generation at the SBST 2022 tool competition

Autoren: Derakhshanfar, Pouria; Devroey, Xavier
Veröffentlicht in: Proceedings of the 2022 IEEE/ACM 15th International Workshop on Search-Based Software Testing (SBST), 2022
Herausgeber: IEEE
DOI: 10.1145/3526072.3527528

Summary of Search-based Crash Reproduction using Behavioral Model Seeding

Autoren: Derakhshanfar, Pouria; Devroey, Xavier; Perrouin, Gilles; Zaidman, Andy; Deursen, Arie Van
Veröffentlicht in: 14th IEEE Conference on Software Testing, Verification and Validation (ICST), 2021
Herausgeber: IEEE
DOI: 10.1109/icst49551.2021.00039

An Empirical Investigation of Relevant Changes and Automation Needs in Modern Code Review

Autoren: Panichella, S., Zaugg, N.
Veröffentlicht in: Empirical Software Engineering, 2021
Herausgeber: Springer
DOI: 10.1007/s10664-020-09870-3

Diversity-guided Search Exploration for Self-driving Cars Test Generation through Frenet Space Encoding

Autoren: Blattner, Timo; Birchler, Christian; Kehrer, Timo; Panichella, Sebastiano
Veröffentlicht in: Arxiv, 2024
Herausgeber: Cornel University
DOI: 10.48550/arxiv.2401.14682

Hybrid Multi-level Crossover for Unit Test Case Generation

Autoren: Olsthoorn, Mitchell; Derakhshanfar, Pouria; Panichella, Annibale
Veröffentlicht in: arXiv, 2021
Herausgeber: Cornel University
DOI: 10.48550/arxiv.2108.05466

Problems and Solutions in Applying Continuous Integration and Delivery to 20 Open-Source Cyber-Physical Systems

Autoren: Fiorella Zampetti, Vittoria Nardone, Massimiliano Di Penta
Veröffentlicht in: Proceedings 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR), 2022
Herausgeber: IEEE

Simulation-based Test Case Generation for Unmanned Aerial Vehicles in the Neighborhood of Real Flights

Autoren: Sajad Khatiri; Sebastiano Panichella; Paolo Tonella
Veröffentlicht in: 2023 IEEE Conference on Software Testing, Verification and Validation (ICST), 2022
Herausgeber: IEEE
DOI: 10.1109/icst57152.2023.00034

TEASER: Simulation-Based CAN Bus Regression Testing for Self-Driving Cars Software

Autoren: Birchler, Christian; Rohrbach, Cyrill; Kim, Hyeongkyun; Gambi, Alessio; Liu, Tianhai; Horneber, Jens; Kehrer, Timo; Panichella, Sebastiano
Veröffentlicht in: IEEE International Conference on Automated Software Engineering (ASE), 2023
Herausgeber: IEEE
DOI: 10.1109/ase56229.2023.00154

Cost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-Scissor

Autoren: Christian Birchler; Nicolas Ganz; Sajad Khatiri; Alessio Gambi; Sebastiano Panichella
Veröffentlicht in: Proceedings 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering, 2022
Herausgeber: IEEE
DOI: 10.1109/saner53432.2022.00030

Towards Log Slicing

Autoren: Joshua Heneage Dawes; Donghwan Shin; Domenico Bianculli
Veröffentlicht in: Proceedings Fundamental Approaches to Software Engineering, 2023, ISBN 978-3-031-30826-0
Herausgeber: Springer
DOI: 10.1007/978-3-031-30826-0_14

NLBSE'22 tool competition

Autoren: Kallis, Rafael; Chaparro, Oscar; Di Sorbo, Andrea; Panichella, Sebastiano
Veröffentlicht in: 2022 IEEE/ACM 1st International Workshop on Natural Language-Based Software Engineering (NLBSE), 2022
Herausgeber: IEEE
DOI: 10.1145/3528588.3528664

Predicting issue types on GitHub

Autoren: Rafael Kallis; Andrea Di Sorbo; Gerardo Canfora; Sebastiano Panichella
Veröffentlicht in: arXiv, 2021
Herausgeber: Cornel University
DOI: 10.48550/arxiv.2107.09936

SBFT Tool Competition 2024 - Python Test Case Generation Track

Autoren: Erni, Nicolas; Ali Mohammed, Al-Ameen Mohammed; Birchler, Christian; Derakhshanfar, Pouria; Lukasczyk, Stephan; Panichella, Sebastiano
Veröffentlicht in: Proceedings of the 17th International Workshop on Search-Based and Fuzz Testing (SBFT@ICSE 2024), 2024
Herausgeber: Cornel University
DOI: 10.5281/zenodo.10554259

Toward Automatically Completing GitHub Workflows

Autoren: Antonio Mastropaolo; Fiorella Zampetti; Gabriele Bavota; Massimiliano Di Penta
Veröffentlicht in: ICSE '24: Proceedings of the 46th IEEE/ACM International Conference on Software Engineering, Ausgabe 30, 2024
Herausgeber: ACM
DOI: 10.1145/3597503.3623351

SBST Tool Competition 2021

Autoren: Vincenzo Riccio; Fiorella Zampetti; Alessio Gambi; Sebastiano Panichella
Veröffentlicht in: IEEE/ACM 14th International Workshop on Search-Based Software Testing (SBST), 2021
Herausgeber: IEEE
DOI: 10.1109/sbst52555.2021.00011

Specifying Properties over Inter-procedural, Source Code Level Behaviour of Programs

Autoren: Joshua Heneage Dawes & Domenico Bianculli
Veröffentlicht in: Proceedings International Conference on Runtime Verification, 2021
Herausgeber: Springer

CI/CD Pipelines Evolution and Restructuring: A Qualitative and Quantitative Study

Autoren: Fiorella Zampetti, Salvatore Geremia, Gabriele Bavota,Massimiliano Di Penta
Veröffentlicht in: Proceedings 2021 IEEE International Conference on Software Maintenance and Evolution, 2021
Herausgeber: IEEE
DOI: 10.1109/icsme52107.2021.00048

Basic block coverage for search-based unit testing and crash reproduction

Autoren: Pouria Derakhshanfar; Xavier Devroey; Andy Zaidman
Veröffentlicht in: Empirical Software Engineering, 2022, ISSN 1382-3256
Herausgeber: Kluwer Academic Publishers
DOI: 10.1007/s10664-022-10155-0

Systematic Evaluation of Deep Learning Models for Failure Prediction

Autoren: Hadadi, Fatemeh; Dawes, Joshua H.; Shin, Donghwan; Bianculli, Domenico; Briand, Lionel
Veröffentlicht in: Empiracal Software Engineering, 2024, ISSN 1382-3256
Herausgeber: Kluwer Academic Publishers
DOI: 10.48550/arxiv.2303.07230

JUGE: An Infrastructure for Benchmarking Java Unit Test Generators

Autoren: Devroey, Xavier; Gambi, Alessio; Galeotti, Juan Pablo; Just, René; Kifetew, Fitsum; Panichella, Annibale; Panichella, Sebastiano
Veröffentlicht in: Software Testing, Verification and Reliability, 2022, Seite(n) 00:1–22, ISSN 1099-1689
Herausgeber: Wiley
DOI: 10.48550/arxiv.2106.07520

Trace Diagnostics for Signal-Based Temporal Properties

Autoren: Chaima Boufaied; Claudio Menghi; Domenico Bianculli; Lionel C. Briand
Veröffentlicht in: Transactions on Software Engineering, 2023, ISSN 0098-5589
Herausgeber: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tse.2023.3242588

Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms

Autoren: Andrea Di Sorbo; Fiorella Zampetti; Aaron Visaggio; Massimiliano Di Penta; Sebastiano Panichella
Veröffentlicht in: Transactions on Software Engineering and Methodology, 2023, ISSN 1049-331X
Herausgeber: Association for Computing Machinary, Inc.
DOI: 10.1145/3564821

Continuous Integration and Delivery Practices for Cyber-Physical Systems: An Interview-Based Study

Autoren: Fiorella Zampetti; Damian Tamburri; Sebastiano Panichella; Annibale Panichella; Gerardo Canfora; Massimiliano Di Penta
Veröffentlicht in: Transactions on Software Engineering and Methodology, 2023, ISSN 1049-331X
Herausgeber: Association for Computing Machinary, Inc.
DOI: 10.1145/3571854

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

Autoren: Christian Birchler; Sajad Khatiri; Bill Bosshard; Alessio Gambi; Sebastiano Panichella
Veröffentlicht in: Empirical Software Engineering, 2023, ISSN 1382-3256
Herausgeber: Kluwer Academic Publishers
DOI: 10.1007/s10664-023-10286-y

Test smells 20 years later: detectability, validity, and reliability

Autoren: Annibale Panichella; Sebastiano Panichella; Gordon Fraser; Anand Ashok Sawant; Vincent J. Hellendoorn
Veröffentlicht in: Empirical Software Engineering, 2022, ISSN 1382-3256
Herausgeber: Kluwer Academic Publishers
DOI: 10.1007/s10664-022-10207-5

An empirical characterization of software bugs in open-source Cyber–Physical Systems

Autoren: Fiorella Zampetti; Ritu Kapur; Massimiliano Di Penta; Sebastiano Panichella
Veröffentlicht in: Journal of Systems and Software, 2022, ISSN 0164-1212
Herausgeber: Elsevier BV
DOI: 10.1016/j.jss.2022.111425

Metamorphic Testing for Web System Security

Autoren: Nazanin Bayati Chaleshtari; Fabrizio Pastore; Arda Goknil; Lionel C. Briand
Veröffentlicht in: Transactions on Software Engineering, 2023, ISSN 1939-3520
Herausgeber: IEEE
DOI: 10.1109/tse.2023.3256322

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

Autoren: Pooja Rani; Arianna Blasi; Nataliia Stulova; Sebastiano Panichella; Alessandra Gorla; Oscar Nierstrasz
Veröffentlicht in: Journal of Systems and Software, 2023, ISSN 0164-1212
Herausgeber: Elsevier BV
DOI: 10.1016/j.jss.2022.111515

Generating Class-Level Integration Tests Using Call Site Information

Autoren: Pouria Derakhshanfar; Xavier Devroey; Annibale Panichella; Andy Zaidman; Arie van Deursen
Veröffentlicht in: IEEE Transactions on Software Engineering, 2022, ISSN 0098-5589
Herausgeber: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tse.2022.3209625

Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems

Autoren: Ul Haq, Fitash; Shin, Donghwan; Briand, Lionel
Veröffentlicht in: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE), 2023, ISSN 1558-1225
Herausgeber: IEEE
DOI: 10.1109/icse48619.2023.00155

Single and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environments

Autoren: Christian Birchler; Sajad Khatiri; Pouria Derakhshanfar; Sebastiano Panichella; Annibale Panichella
Veröffentlicht in: ACM Transactions on Software Engineering and Methodology, Ausgabe 1049331X, 2022, ISSN 1049-331X
Herausgeber: Association for Computing Machinary, Inc.
DOI: 10.1145/3533818

“Won’t We Fix this Issue?” Qualitative characterization and automated identification of wontfix issues on GitHub

Autoren: Gerardo Canfora; Andrea Di Sorbo; Sebastiano Panichella
Veröffentlicht in: Information and Software Technology, 2021, ISSN 0950-5849
Herausgeber: Elsevier BV
DOI: 10.1016/j.infsof.2021.106665

Leistungen

Veröffentlichungen

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