Model-Based Testing IoT Communication via Active Automata Learning

M. Tappler; B. K. Aichernig; R. Bloem

doi:10.1109/ICST.2017.32

Model-Based Testing IoT Communication via Active Automata Learning

M. Tappler
, B. K. Aichernig
, R. Bloem

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

This paper presents a learning-based approach to detecting failures in reactive systems. The technique is based on inferring models of multiple implementations of a common specification which are pair-wise cross-checked for equivalence. Any counterexample to equivalence is flagged as suspicious and has to be analysed manually. Hence, it is possible to find possible failures in a semi-automatic way without prior modelling. We show that the approach is effective by means of a case study. For this case study, we carried out experiments in which we learned models of five implementations of MQTT brokers/servers, a protocol used in the Internet of Things. Examining these models, we found several violations of the MQTT specification. All but one of the considered implementations showed faulty behaviour. In the analysis, we discuss effectiveness and also issues we faced.

Original language	English
Title of host publication	2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)
Pages	276-287
Number of pages	12
DOIs	https://doi.org/10.1109/ICST.2017.32
Publication status	Published - 2017
Event	10th IEEE International Conference on Software Testing, Verification and Validation (ICST 2017): ICST 2017 - Tokyo, Japan Duration: 13 Mar 2017 → 17 Mar 2017 http://aster.or.jp/conference/icst2017/

Conference

Conference	10th IEEE International Conference on Software Testing, Verification and Validation (ICST 2017)
Abbreviated title	ICST 2017
Country/Territory	Japan
City	Tokyo
Period	13/03/17 → 17/03/17
Internet address	http://aster.or.jp/conference/icst2017/

Keywords

Internet of Things
Learning automata
Protocols
Testing
MQTT
automata learning
internet of things
model inference
model-based testing

Fields of Expertise

Information, Communication & Computing

Access to Document

10.1109/ICST.2017.32

Dependable Internet of Things
Pernkopf, F. (Contact person), Zakany, N. (Contact person), Eichlseder, M. (Contact person), Saukh, O. (Contact person), Mangard, S. (Contact person), Steinbauer-Wagner, G. (Contact person), Knoll, C. (Attendee / Assistant), Tranninger, M. (Attendee / Assistant), Römer, K. U. (Consortium manager resp. coordinator of internal research units), Rath, M. (Attendee / Assistant), Kubin, G. (Contact person), Horn, M. (Contact person), Tappler, M. (Attendee / Assistant), Bloem, R. (Consortium manager resp. coordinator of internal research units), Weiser, S. (Attendee / Assistant), Leitinger, E. (Contact person), Ebrahimi, M. (Attendee / Assistant), Aichernig, B. (Contact person), Malenko, M. (Attendee / Assistant), Steinberger, M. (Contact person), Großwindhager, B. (Attendee / Assistant), Baunach, M. C. (Contact person), Witrisal, K. (Contact person), Teschl, R. (Contact person), Boano, C. A. (Contact person), Alothman Alterkawi, A. B. (Attendee / Assistant), Bösch, W. (Contact person) & Grosinger, J. (Contact person)
1/01/16 → 31/03/22
Project: Research project

Cite this

Tappler, M, Aichernig, BK & Bloem, R 2017, Model-Based Testing IoT Communication via Active Automata Learning. in 2017 IEEE International Conference on Software Testing, Verification and Validation (ICST). pp. 276-287, 10th IEEE International Conference on Software Testing, Verification and Validation (ICST 2017), Tokyo, Japan, 13/03/17. https://doi.org/10.1109/ICST.2017.32

@inproceedings{032db6508e6e487ca2aefd4ba7fbe403,

title = "Model-Based Testing IoT Communication via Active Automata Learning",

abstract = "This paper presents a learning-based approach to detecting failures in reactive systems. The technique is based on inferring models of multiple implementations of a common specification which are pair-wise cross-checked for equivalence. Any counterexample to equivalence is flagged as suspicious and has to be analysed manually. Hence, it is possible to find possible failures in a semi-automatic way without prior modelling. We show that the approach is effective by means of a case study. For this case study, we carried out experiments in which we learned models of five implementations of MQTT brokers/servers, a protocol used in the Internet of Things. Examining these models, we found several violations of the MQTT specification. All but one of the considered implementations showed faulty behaviour. In the analysis, we discuss effectiveness and also issues we faced.",

keywords = "Internet of Things, Learning automata, Protocols, Testing, MQTT, automata learning, internet of things, model inference, model-based testing",

author = "M. Tappler and Aichernig, \{B. K.\} and R. Bloem",

year = "2017",

doi = "10.1109/ICST.2017.32",

language = "English",

pages = "276--287",

booktitle = "2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)",

note = "10th IEEE International Conference on Software Testing, Verification and Validation (ICST 2017) : ICST 2017, ICST 2017 ; Conference date: 13-03-2017 Through 17-03-2017",

url = "http://aster.or.jp/conference/icst2017/",

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AU - Tappler, M.

AU - Aichernig, B. K.

AU - Bloem, R.

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N2 - This paper presents a learning-based approach to detecting failures in reactive systems. The technique is based on inferring models of multiple implementations of a common specification which are pair-wise cross-checked for equivalence. Any counterexample to equivalence is flagged as suspicious and has to be analysed manually. Hence, it is possible to find possible failures in a semi-automatic way without prior modelling. We show that the approach is effective by means of a case study. For this case study, we carried out experiments in which we learned models of five implementations of MQTT brokers/servers, a protocol used in the Internet of Things. Examining these models, we found several violations of the MQTT specification. All but one of the considered implementations showed faulty behaviour. In the analysis, we discuss effectiveness and also issues we faced.

AB - This paper presents a learning-based approach to detecting failures in reactive systems. The technique is based on inferring models of multiple implementations of a common specification which are pair-wise cross-checked for equivalence. Any counterexample to equivalence is flagged as suspicious and has to be analysed manually. Hence, it is possible to find possible failures in a semi-automatic way without prior modelling. We show that the approach is effective by means of a case study. For this case study, we carried out experiments in which we learned models of five implementations of MQTT brokers/servers, a protocol used in the Internet of Things. Examining these models, we found several violations of the MQTT specification. All but one of the considered implementations showed faulty behaviour. In the analysis, we discuss effectiveness and also issues we faced.

KW - Internet of Things

KW - Learning automata

KW - Protocols

KW - Testing

KW - MQTT

KW - automata learning

KW - internet of things

KW - model inference

KW - model-based testing

U2 - 10.1109/ICST.2017.32

DO - 10.1109/ICST.2017.32

M3 - Conference paper

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BT - 2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)

T2 - 10th IEEE International Conference on Software Testing, Verification and Validation (ICST 2017)

Y2 - 13 March 2017 through 17 March 2017

ER -

Model-Based Testing IoT Communication via Active Automata Learning

Abstract

Conference

Keywords

Fields of Expertise

Access to Document

Fingerprint

Projects

Dependable Internet of Things

Cite this