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Publications (10 of 36) Show all publications
Khosrowjerdi, H. & Meinke, K. (2018). Learning-Based testing for autonomous systems using spatial and temporal requirements. In: MASES 2018 - Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis, co-located with ASE 2018: . Paper presented at 1st International Workshop on Machine Learning and Software Engineering in Symbiosis, MASES 2018, co-located with ASE 2018 Conference, 3 September 2018 (pp. 6-15). Association for Computing Machinery, Inc
Open this publication in new window or tab >>Learning-Based testing for autonomous systems using spatial and temporal requirements
2018 (English)In: MASES 2018 - Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis, co-located with ASE 2018, Association for Computing Machinery, Inc , 2018, p. 6-15Conference paper, Published paper (Refereed)
Abstract [en]

Cooperating cyber-physical systems-of-systems (CO-CPS) such as vehicle platoons, robot teams or drone swarms usually have strict safety requirements on both spatial and temporal behavior. Learning-based testing is a combination of machine learning and model checking that has been successfully used for black-box requirements testing of cyber-physical systems-of-systems. We present an overview of research in progress to apply learning-based testing to evaluate spatio-temporal requirements on autonomous systems-of-systems through modeling and simulation.

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc, 2018
Keywords
Automotive software, Black-box testing, Learningbased testing, Machine learning, Model-based testing, Requirements testing, Spatio-temporal logic, Artificial intelligence, Cyber Physical System, Embedded systems, Learning systems, Model checking, System of systems, Systems engineering, Autonomous systems, Cyber physical systems (CPSs), Model and simulation, Model based testing, Safety requirements, Spatio temporal, Temporal behavior
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-247188 (URN)10.1145/3243127.3243129 (DOI)2-s2.0-85055868610 (Scopus ID)9781450359726 (ISBN)
Conference
1st International Workshop on Machine Learning and Software Engineering in Symbiosis, MASES 2018, co-located with ASE 2018 Conference, 3 September 2018
Note

QC 20190506

Available from: 2019-05-06 Created: 2019-05-06 Last updated: 2019-05-06Bibliographically approved
Meinke, K. (2018). Learning-based testing: Recent progress and future prospects. In: International Dagstuhl Seminar 16172 Machine Learning for Dynamic Software Analysis: Potentials and Limits, 2016: . Paper presented at International Dagstuhl Seminar 16172 Machine Learning for Dynamic Software Analysis: Potentials and Limits, 2016, Wadern, Germany, 24 April 2016 through 27 April 2016 (pp. 53-73). Springer, 11026
Open this publication in new window or tab >>Learning-based testing: Recent progress and future prospects
2018 (English)In: International Dagstuhl Seminar 16172 Machine Learning for Dynamic Software Analysis: Potentials and Limits, 2016, Springer, 2018, Vol. 11026, p. 53-73Conference paper, Published paper (Refereed)
Abstract [en]

We present a survey of recent progress in the area of learning-based testing (LBT). The emphasis is primarily on fundamental concepts and theoretical principles, rather than applications and case studies. After surveying the basic principles and a concrete implementation of the approach, we describe recent directions in research such as: quantifying the hardness of learning problems, over-approximation methods for learning, and quantifying the power of model checker generated test cases. The common theme underlying these research directions is seen to be metrics for model convergence. Such metrics enable a precise, general and quantitative approach to both speed of learning and test coverage. Moreover, quantitative approaches to black-box test coverage serve to distinguish LBT from alternative approaches such as random and search-based testing. We conclude by outlining some prospects for future research.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 11026
National Category
Learning
Identifiers
urn:nbn:se:kth:diva-233738 (URN)10.1007/978-3-319-96562-8_2 (DOI)000476941200002 ()2-s2.0-85051106770 (Scopus ID)9783319965611 (ISBN)
Conference
International Dagstuhl Seminar 16172 Machine Learning for Dynamic Software Analysis: Potentials and Limits, 2016, Wadern, Germany, 24 April 2016 through 27 April 2016
Note

QC 20180830

Available from: 2018-08-30 Created: 2018-08-30 Last updated: 2019-08-09Bibliographically approved
Bennaceur, A. & Meinke, K. (2018). Machine learning for software analysis: Models, methods, and applications. In: International Dagstuhl Seminar 16172 Machine Learning for Dynamic Software Analysis: Potentials and Limits, 2016: . Paper presented at International Dagstuhl Seminar 16172 Machine Learning for Dynamic Software Analysis: Potentials and Limits, 2016; Wadern; Germany; 24 April 2016 through 27 April 2016 (pp. 3-49). Springer, 11026
Open this publication in new window or tab >>Machine learning for software analysis: Models, methods, and applications
2018 (English)In: International Dagstuhl Seminar 16172 Machine Learning for Dynamic Software Analysis: Potentials and Limits, 2016, Springer, 2018, Vol. 11026, p. 3-49Conference paper, Published paper (Refereed)
Abstract [en]

Machine Learning (ML) is the discipline that studies methods for automatically inferring models from data. Machine learning has been successfully applied in many areas of software engineering including: behaviour extraction, testing and bug fixing. Many more applications are yet to be defined. Therefore, a better fundamental understanding of ML methods, their assumptions and guarantees can help to identify and adopt appropriate ML technology for new applications. In this chapter, we present an introductory survey of ML applications in software engineering, classified in terms of the models they produce and the learning methods they use. We argue that the optimal choice of an ML method for a particular application should be guided by the type of models one seeks to infer. We describe some important principles of ML, give an overview of some key methods, and present examples of areas of software engineering benefiting from ML. We also discuss the open challenges for reaching the full potential of ML for software engineering and how ML can benefit from software engineering methods.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 11026
Keywords
Machine learning, Software engineering
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-233742 (URN)10.1007/978-3-319-96562-8_1 (DOI)000476941200001 ()2-s2.0-85051142497 (Scopus ID)9783319965611 (ISBN)
Conference
International Dagstuhl Seminar 16172 Machine Learning for Dynamic Software Analysis: Potentials and Limits, 2016; Wadern; Germany; 24 April 2016 through 27 April 2016
Funder
Vinnova, 2013-05608 VIRTUESEU, European Research Council, 291652 (ASAP), and the EPSRC EP/R013144/1 SAUSE project
Note

QC 20180831

Available from: 2018-08-31 Created: 2018-08-31 Last updated: 2019-08-12Bibliographically approved
Meinke, K. & Bennaceur, A. (2018). Machine learning for software engineering: Models, methods, and applications. In: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings: . Paper presented at 40th ACM/IEEE International Conference on Software Engineering, ICSE 2018, 27 May 2018 through 3 June 2018 (pp. 548-549). IEEE Computer Society
Open this publication in new window or tab >>Machine learning for software engineering: Models, methods, and applications
2018 (English)In: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, IEEE Computer Society, 2018, p. 548-549Conference paper, Published paper (Refereed)
Abstract [en]

Machine Learning (ML) is the discipline that studies methods for automatically inferring models from data. Machine learning has been successfully applied in many areas of software engineering ranging from behaviour extraction, to testing, to bug fixing. Many more applications are yet be defined. However, a better understanding of ML methods, their assumptions and guarantees would help software engineers adopt and identify the appropriate methods for their desired applications. We argue that this choice can be guided by the models one seeks to infer. In this technical briefing, we review and reflect on the applications of ML for software engineering organised according to the models they produce and the methods they use. We introduce the principles of ML, give an overview of some key methods, and present examples of areas of software engineering benefiting from ML. We also discuss the open challenges for reaching the full potential of ML for software engineering and how ML can benefit from software engineering methods.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Series
Proceedings - International Conference on Software Engineering, ISSN 0270-5257
National Category
Software Engineering
Identifiers
urn:nbn:se:kth:diva-238226 (URN)10.1145/3183440.3183461 (DOI)000450109000227 ()2-s2.0-85049687459 (Scopus ID)9781450356633 (ISBN)
Conference
40th ACM/IEEE International Conference on Software Engineering, ICSE 2018, 27 May 2018 through 3 June 2018
Note

QC 20181114

Available from: 2018-11-14 Created: 2018-11-14 Last updated: 2018-12-04Bibliographically approved
Bergenhem, C., Meinke, K. & Ström, F. (2018). Quantitative Safety Analysis of a Coordinated Emergency Brake Protocol for Vehicle Platoons. In: Tiziana Margaria, Bernhard Steffen (Ed.), Leveraging Applications of Formal Methods, Verification and Validation. Distributed Systems - 8th International Symposium, ISoLA 2018: . Paper presented at 8th International Symposium, ISoLA 2018, Limassol, Cyprus (pp. 386-404). Springer, 11246
Open this publication in new window or tab >>Quantitative Safety Analysis of a Coordinated Emergency Brake Protocol for Vehicle Platoons
2018 (English)In: Leveraging Applications of Formal Methods, Verification and Validation. Distributed Systems - 8th International Symposium, ISoLA 2018 / [ed] Tiziana Margaria, Bernhard Steffen, Springer, 2018, Vol. 11246, p. 386-404Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a general methodology to estimate safety related parameter values of cooperative cyber-physical system-of- systems. As a case study, we consider a vehicle platoon model equipped with a novel distributed protocol for coordinated emergency braking. The estimation methodology is based on learning-based testing; which is an approach to automated requirements testing that combines machine learning with model checking.

Our methodology takes into account vehicle dynamics, control algorithm design, inter-vehicle communication protocols and environmental factors such as message packet loss rates. Empirical measurements from road testing of vehicle-to-vehicle communication in a platoon are modeled and used in our case study. We demonstrate that the minimum global time headway for our platoon model equipped with the CEBP function scales well with respect to platoon size.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science
Keywords
vehicle platoon, learning-based testing, Co-CPS, safety boundaries, quantitative analysis, coordinated braking
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-257965 (URN)10.1007/978-3-030-03424-5\_26 (DOI)978-3-030-03423-8 (ISBN)
Conference
8th International Symposium, ISoLA 2018, Limassol, Cyprus
Funder
EU, Horizon 2020, 692529-2
Note

QC 20190925

Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-09-25Bibliographically approved
Khosrowjerdi, H., Meinke, K. & Rasmusson, A. (2018). Virtualized-Fault Injection Testing: a Machine Learning Approach. In: 2018 IEEE 11TH INTERNATIONAL CONFERENCE ON SOFTWARE TESTING, VERIFICATION AND VALIDATION (ICST): . Paper presented at 11th IEEE International Conference on Software Testing, Verification and Validation (ICST), APR 09-13, 2018, Vasteras, SWEDEN (pp. 297-308). IEEE
Open this publication in new window or tab >>Virtualized-Fault Injection Testing: a Machine Learning Approach
2018 (English)In: 2018 IEEE 11TH INTERNATIONAL CONFERENCE ON SOFTWARE TESTING, VERIFICATION AND VALIDATION (ICST), IEEE , 2018, p. 297-308Conference paper, Published paper (Refereed)
Abstract [en]

We introduce a new methodology for virtualized fault injection testing of safety critical embedded systems. This approach fully automates the key steps of test case generation, fault injection and verdict construction. We use machine learning to reverse engineer models of the system under test. We use model checking to generate test verdicts with respect to safety requirements formalised in temporal logic. We exemplify our approach by implementing a tool chain based on integrating the QEMU hardware emulator, the GNU debugger GDB and the LBTest requirements testing tool. This tool chain is then evaluated on two industrial safety critical applications from the automotive sector.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE International Conference on Software Testing Verification and Validation, ISSN 2381-2834
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-231653 (URN)10.1109/ICST.2018.00037 (DOI)000435006300027 ()2-s2.0-85048401472 (Scopus ID)978-1-5386-5012-7 (ISBN)
Conference
11th IEEE International Conference on Software Testing, Verification and Validation (ICST), APR 09-13, 2018, Vasteras, SWEDEN
Note

QC 20180905

Available from: 2018-09-05 Created: 2018-09-05 Last updated: 2019-03-22Bibliographically approved
Meinke, K. (2017). Learning-Based testing of cyber-physical systems-of-systems: A platooning study. In: 14th European Workshop on Computer Performance Engineering, EPEW 2017: . Paper presented at 14th European Workshop on Computer Performance Engineering, EPEW 2017, Berlin, Germany, 7 September 2017 through 8 September 2017 (pp. 135-151). Springer, 10497
Open this publication in new window or tab >>Learning-Based testing of cyber-physical systems-of-systems: A platooning study
2017 (English)In: 14th European Workshop on Computer Performance Engineering, EPEW 2017, Springer, 2017, Vol. 10497, p. 135-151Conference paper, Published paper (Refereed)
Abstract [en]

Learning-based testing (LBT) is a paradigm for fully automated requirements testing that combines machine learning with model-checking techniques. LBT has been shown to be effective for unit and integration testing of safety critical components in cyber-physical systems, e.g. automotive ECU software. We consider the challenges faced, and some initial results obtained in an effort to scale up LBT to testing co-operative open cyber-physical systems-of-systems (CO-CPS). For this we focus on a case study of testing safety and performance properties of multi-vehicle platoons.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10497
Keywords
Cyber-physical system, Learning-based testing, Machine learning, Model-based testing, Platooning, Requirements testing, System-of-systems
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-216358 (URN)10.1007/978-3-319-66583-2_9 (DOI)2-s2.0-85029453120 (Scopus ID)9783319665825 (ISBN)
Conference
14th European Workshop on Computer Performance Engineering, EPEW 2017, Berlin, Germany, 7 September 2017 through 8 September 2017
Funder
Vinnova, 2013-05608 Virtues
Note

QC 20171020

Available from: 2017-10-20 Created: 2017-10-20 Last updated: 2019-05-20Bibliographically approved
Howar, F., Meinke, K. & Rausch, A. (2016). Learning Systems: Machine-Learning in Software Products and Learning-Based Analysis of Software Systems Special Track at ISoLA 2016. In: LEVERAGING APPLICATIONS OF FORMAL METHODS, VERIFICATION AND VALIDATION: DISCUSSION, DISSEMINATION, APPLICATIONS, ISOLA 2016, PT II. Paper presented at 7th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA), OCT 10-14, 2016, Corfu, GREECE (pp. 651-654).
Open this publication in new window or tab >>Learning Systems: Machine-Learning in Software Products and Learning-Based Analysis of Software Systems Special Track at ISoLA 2016
2016 (English)In: LEVERAGING APPLICATIONS OF FORMAL METHODS, VERIFICATION AND VALIDATION: DISCUSSION, DISSEMINATION, APPLICATIONS, ISOLA 2016, PT II, 2016, p. 651-654Conference paper, Published paper (Refereed)
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9953
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-199815 (URN)10.1007/978-3-319-47169-3_50 (DOI)000389942800051 ()2-s2.0-84993989937 (Scopus ID)978-3-319-47169-3; 978-3-319-47168-6 (ISBN)
Conference
7th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA), OCT 10-14, 2016, Corfu, GREECE
Note

QC 20170116

Available from: 2017-01-16 Created: 2017-01-16 Last updated: 2017-01-16Bibliographically approved
Shaolin, H., Meinke, K. & Xinfeng, W. (2016). Simulation-Based Boundary Testing of Software with Its Applications. In: Jiang, Z Y (Ed.), Proceedings of the 2016 6th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2016): . Paper presented at 6th International Conference on Advanced Design and Manufacturing Engineering (ICADME), JUL 23-24, 2016, Zhuhai, PEOPLES R CHINA (pp. 741-744). ATLANTIS PRESS
Open this publication in new window or tab >>Simulation-Based Boundary Testing of Software with Its Applications
2016 (English)In: Proceedings of the 2016 6th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2016) / [ed] Jiang, Z Y, ATLANTIS PRESS , 2016, p. 741-744Conference paper, Published paper (Refereed)
Abstract [en]

To test the boundary values of input variables is very useful but difficult when designing and debugging a software system for scientific computations. In this paper, the model-based method and the simulation-based method are integrated to form a practical approach to deal with this troublesome problem of boundary testing. After selectively building a model library, the testing algorithms are designed in detail are used in designing and evaluating the systems for exterior tracking and post processing of measurement data.

Place, publisher, year, edition, pages
ATLANTIS PRESS, 2016
Series
AER-Advances in Engineering Research, ISSN 2352-5401 ; 96
Keywords
Software Testing, Boundary Testing, Model Based Approach
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-242727 (URN)000392735700140 ()978-94-6252-249-7 (ISBN)
Conference
6th International Conference on Advanced Design and Manufacturing Engineering (ICADME), JUL 23-24, 2016, Zhuhai, PEOPLES R CHINA
Note

QC 20190219

Available from: 2019-02-19 Created: 2019-02-19 Last updated: 2019-02-19Bibliographically approved
Chen, D., Meinke, K., Östberg, K., Asplund, F. & Baumann, C. (2015). A Knowledge-in-the-Loop Approach to Integrated Safety&Security for Cooperative System-of-Systems. In: IEEE Seventh International Conference on Intelligent Computing and Information Systems: . Paper presented at International Symposium on Knowledge Engineering for Decision Support Systems, IEEE Seventh International Conference on Intelligent Computing and Information Systems, ICICIS’15, , Cairo, Egypt. December 12-14, 2015.. IEEE
Open this publication in new window or tab >>A Knowledge-in-the-Loop Approach to Integrated Safety&Security for Cooperative System-of-Systems
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2015 (English)In: IEEE Seventh International Conference on Intelligent Computing and Information Systems, IEEE , 2015Conference paper, Published paper (Refereed)
Abstract [en]

A system-of-systems (SoS) is inherently open inconfiguration and evolutionary in lifecycle. For the nextgeneration of cooperative cyber-physical system-of-systems,safety and security constitute two key issues of public concernthat affect the deployment and acceptance. In engineering, theopenness and evolutionary nature also entail radical paradigmshifts. This paper presents one novel approach to thedevelopment of qualified cyber-physical system-of-systems, withCooperative Intelligent Transport Systems (C-ITS) as one target.The approach, referred to as knowledge-in-the-loop, aims toallow a synergy of well-managed lifecycles, formal qualityassurance, and smart system features. One research goal is toenable an evolutionary development with continuous andtraceable flows of system rationale from design-time to postdeploymenttime and back, supporting automated knowledgeinference and enrichment. Another research goal is to develop aformal approach to risk-aware dynamic treatment of safety andsecurity as a whole in the context of system-of-systems. Key basetechnologies include: (1) EAST-ADL for the consolidation ofsystem-wide concerns and for the creation of an ontology foradvanced run-time decisions, (2) Learning Based-Testing for runtimeand post-deployment model inference, safety monitoringand testing, (3) Provable Isolation for run-time attack detectionand enforcement of security in real-time operating systems.

Place, publisher, year, edition, pages
IEEE, 2015
Keywords
systems-of-systems, cyber-physical system, ontology, knowledge modeling, machine learning, safety, security, modelbased development, verification and validation, quality-of-service
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science; Industrial Engineering and Management; Information and Communication Technology; Machine Design; Transport Science; Planning and Decision Analysis
Identifiers
urn:nbn:se:kth:diva-177573 (URN)10.1109/IntelCIS.2015.7397237 (DOI)000380470400045 ()2-s2.0-84969949567 (Scopus ID)978-150901949-6 (ISBN)
External cooperation:
Conference
International Symposium on Knowledge Engineering for Decision Support Systems, IEEE Seventh International Conference on Intelligent Computing and Information Systems, ICICIS’15, , Cairo, Egypt. December 12-14, 2015.
Projects
Vinnova SAFERVinnova FFI VIRTUESVinnova FFI ITRANSITEIT Digital CPS for Smart Factories.
Funder
VINNOVA
Note

QC 20160905

Available from: 2015-11-24 Created: 2015-11-24 Last updated: 2016-09-05Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9706-5008

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