Return to search

Data-driven test case design of automatic test cases using Markov chains and a Markov chain Monte Carlo method / Datadriven testfallsdesign av automatiska testfall med Markovkedjor och en Markov chain Monte Carlo-metod

Large and complex software that is frequently changed leads to testing challenges. It is well established that the later a fault is detected in software development, the more it costs to fix. This thesis aims to research and develop a method of generating relevant and non-redundant test cases for a regression test suite, to catch bugs as early in the development process as possible. The research was executed at Axis Communications AB with their products and systems in mind. The approach utilizes user data to dynamically generate a Markov chain model and with a Markov chain Monte Carlo method, strengthen that model. The model generates test case proposals, detects test gaps, and identifies redundant test cases based on the user data and data from a test suite. The sampling in the Markov chain Monte Carlo method can be modified to bias the model for test coverage or relevancy. The model is generated generically and can therefore be implemented in other API-driven systems. The model was designed with scalability in mind and further implementations can be made to increase the complexity and further specialize the model for individual needs.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mau-43498
Date January 2021
CreatorsLindahl, John, Persson, Douglas
PublisherMalmö universitet, Fakulteten för teknik och samhälle (TS), Malmö universitet, Fakulteten för teknik och samhälle (TS)
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

Page generated in 0.0027 seconds