Master Thesis: AI-driven Test Fault Classification

Teknik, data och digitalt · Mjukvaru- och webbutveckling · Mjukvaruutveckling · Maskininlärning

I korthet

This Master Thesis project at Ericsson in Linköping, Sweden, aims to optimize software testing by developing AI-driven methods for automatic identification and classification of test faults. The project will reduce manual effort, create a database of common faults, and investigate automated ticket creation with consistent fault information.

Ansvarsområden

Optimize testing using automated ways to identify and classify faults.
Reduce manual effort in parsing test logs and analyzing failures.
Develop a comprehensive database of common faults.
Investigate how to create automated tickets with necessary and consistent information about the fault.

Krav

Master´s student in computer science, computer engineering or similar.
Background in software testing is preferred.
Background in AI/ML is preferred.

Färdigheter

Artificial IntelligenceMachine LearningAnalysisC++AlgorithmsAgileAcademic WritingAPIArduino Software5GArchitecture

#Master Thesis#AI#Test Fault Classification#Software Testing#Machine Learning#Fault Analysis#Ticket Creation

Om Ericsson

Pioneering technology for connecting billions of people worldwide

Our purpose \nTo create connections that make the unimaginable possible.\n\nOur vision\nA world where limitless connectivity improves lives, redefines business and pioneers a sustainable future.\n\nOur values\nPerseverance, professionalism, respect and integrity.\n\nThe future is a place for purpose & vision – ours are clear, and we invite partners, customers and consumers to join us in our journey. \n\nFor a brighter future. For all. Let's #ImaginePossible

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