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Learning-based testing of automotive ECUs
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Inlärningsbaserad testning av ECU:er (Swedish)
Abstract [en]

LBTest is a learning based-testing tool for black box testing, developed by the software reliability group at KTH. Learning based-testing combines model checking with a learning algorithm that incrementally learns a model of the system under test, which allows for a high degree of automation. This thesis examines the possibilities to use LBTest for testing of electronic control units (ECUs) at Scania. Through two case studies the possibility to formalise ECU requirements and to model ECU applicationsfor LBTest are evaluated. The case studies are followed up with benchmarking against test cases currently in use at Scania. The results of the case studies show that most of the functional requirements can, after reformulation, be formalised for LBTest and that LBTest can find previously undetected defects in ECU software. The benchmarking also shows a high error detection rate for LBTest. Finally, the thesis presents guidelines for requirement formulation and improvements of LBTest are suggested.

Abstract [sv]

LBTest är ett inlärningsbaserat verktyg för black box-testing som har utvecklats av programvarutillförlitghetsgruppen på KTH. Inlärningsbaserad testning kombinerar model checking med en inlärningsalgoritm som stegvis bygger upp en lärd modell av systemet under test, vilket möjliggör en hög grad av automatisering. Denna uppsats undersöker möjligheten att använda LBTest för att testa elektroniska kontrollenheter (ECU:er) på Scania. Genom två fallstudier utvärderas möjligheten att formalisera krav på ECU:er och modellera ECU-applikationer för LBTest. Fallstudierna följs upp med en benchmarking gentemot befintliga testfall på Scania. Resultaten av fallstudierna visar att majoriteten av de funktionella kraven kan formaliseras för LBTest efter en omformulering och att LBTest kan hitta tidigare oupptäckta fel i mjukvaran. Benchmarkingen visar en hög grad av feldetektion för LBTest. I uppsatsen föreslås också riktlinjer för kravformulering och möjliga förbättringar av LBTest.

Place, publisher, year, edition, pages
2016.
Keyword [en]
Software testing
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-199711OAI: oai:DiVA.org:kth-199711DiVA: diva2:1065359
External cooperation
Scania CV AB
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2017-02-02 Created: 2017-01-15 Last updated: 2018-01-13Bibliographically approved

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