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Learning-based Testing of aLarge Scale Django Web Application: An Exploratory Case Study Using LBTest
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Inlärningsbaserad testning av en storskalig Django-webbapplikation (Swedish)
Abstract [en]

Learning-based testing is an emerging software testing paradigm. With it, automatic test case generation is achieved by combining model-based testing with machine learning. Recent research has led to the development of LBTest, a tool that is getting ready for use in the software industry. The tool has mostly been used in small academic case studies and only two previous industrial case studies. This thesis investigated the use of LBTest for testing a large scale web application from the financial IT sector, built using the popular Django framework. When errors were injected into the system under test, LBTest successfully generated test cases showing how they were violating given software requirements. Efficiency problems were discovered and solved in a general way, repeatable for testing any Django system with LBTest.

Abstract [sv]

Inlärningsbaserad testning är ett nytt paradigm för mjukvarutestning. Med det kan automatisk testfallsgenerering uppnås genom att kombinera modell-baserad testning med maskininlärning. Nyare forskning har lett till utvecklingen av LBTest, ett verktyg som börjar bli redo för användning inom mjukvaruindustrin. Verktyget har mest använts i små akademiska fallstudier och endast i två tidigare industriella fallstudier.

Denna rapport undersöker om LBTest kan användas för att testa en storskalig webbapplikation från den finansiella IT-sektorn, byggd med det populära ramverket Django. När fel injicerades i systemet under test genererade LBTest framgångsrikt testfall som visar hur det bröt mot det givna mjukvarukravet. Effektivitetsproblem upptäcktes och löstes på ett generellt sätt, upprepningsbart för att testa andra Django-system med LBTest.

Place, publisher, year, edition, pages
2013.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-142476OAI: oai:DiVA.org:kth-142476DiVA: diva2:703054
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2014-03-11 Created: 2014-03-05 Last updated: 2014-03-11Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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