Model-Based Testing IoT Communication via Active Automata Learning

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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 languageEnglish
Title of host publication2017 IEEE International Conference on Software Testing, Verification and Validation (ICST)
Pages276-287
Number of pages12
DOIs
Publication statusPublished - 2017
Event10th IEEE International Conference on Software Testing, Verification and Validation (ICST 2017) - Tokyo, Japan
Duration: 13 Mar 201717 Mar 2017
http://aster.or.jp/conference/icst2017/

Conference

Conference10th IEEE International Conference on Software Testing, Verification and Validation (ICST 2017)
Abbreviated titleICST 2017
CountryJapan
CityTokyo
Period13/03/1717/03/17
Internet address

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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

Cite this

Tappler, M., Aichernig, B. K., & 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) https://doi.org/10.1109/ICST.2017.32