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Automatic Anomaly Detection in Graphical User Interfaces Using Deep Neural Networks
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Automatisk detektering av avvikelser i grafiska användargränssnitt med hjälp av djupinlärning (Swedish)
Abstract [en]

The automatic detection of code errors is a ubiquitous part of the quality assurance process performed during software development. However, graphical errors that may occur in user interfaces are often detected manually. This report examines if deep neural networks (DNNs), may be used to automatically detect two common types of anomalies present in a graphical user interface. The results point towards this being the case for the particular dataset used in this report.

Abstract [sv]

Automatisk detektering av kodfel är standard i kvalitetsarbetet som utförs vid

mjukvaruveckling. Grafiska fel som kan uppstå i användargränssnitt upptäcks dock ofta manuellt. Den här rapporten undersöker ifall djupa neurala nätverk kan användas för att automatiskt detektera två vanliga fel som uppstår i användargränssnitt. Resultaten indikerar att så är fallet åtminstone för det specifika dataset som används.

Place, publisher, year, edition, pages
2019. , p. 43
Series
TRITA-EECS-EX ; 2019:513
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-264457OAI: oai:DiVA.org:kth-264457DiVA, id: diva2:1373586
External cooperation
Accedo
Educational program
Master of Science in Engineering - Electrical Engineering
Supervisors
Examiners
Available from: 2019-11-27 Created: 2019-11-27

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CiteExportLink to record
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  • apa
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