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Learning-Based Testing of Microservices: An Exploratory Case Study Using LBTest
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Inlärningsbaserad testning av microservices (Swedish)
Abstract [en]

Learning-based testing (LBT) is a relatively new testing paradigm which automatically generates test cases for black-box testing of a system under test (SUT). LBT uses machine learning to model a SUT, and combines this with model-based testing.

This thesis uses LBTest, a research tool created at CSC, in order to apply LBT on a new architectural style of distributed systems called microservices. Two new approaches to using LBT have been implemented to test a commercial product for counter-party credit risk. One approach is to monitor the internal processes to extract the states of the software. The second is based on fault injection on the software level. Errors have been found during the fault injection approach. Lastly, some general recommendations are given on how to implement LBT.

Abstract [sv]

Inlärningsbaserad testning (LBT) är en relativt ny testningsparadigm som automatiskt genererar testfall för black-box-testning av ett system under test (SUT). LBT använder sig av maskininlärning för att modellera ett SUT, och kombinerar det med modellbaserad testning.

I det här examensarbetet används LBTest, ett forskningverktyg skapat på CSC, för att applicera LBT på microservices. Två nya tillvägagångssätt att använda LBT på har implementerats för att testa en kommersiell produkt för uträkning av kreditrisk hos motparter. Ett tillvägagångssätt är att avlyssna interna processer för att extrahera tillstånden hos mjukvaran. Det andra tillvägagångssättet är baserat på felinjicering på mjukvarunivå. Fel har hittats med hjälp av felinjiceringstillvägagångssättet. Som avslutning ges rekommendationer till hur LBT implementeras.

Place, publisher, year, edition, pages
2015.
Keyword [en]
microservices lbtest learning-based testing lbt trioptima
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-172349OAI: oai:DiVA.org:kth-172349DiVA: diva2:847215
External cooperation
TriOptima AB
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2015-08-21 Created: 2015-08-19 Last updated: 2015-08-21Bibliographically approved

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CiteExportLink to record
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