Master Thesis: AI-driven Test Fault Classification

Technology, Data & Digital · Software & Web Development · Software Engineering · Machine Learning

In short

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.

Responsibilities

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.

Requirements

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

Skills

Artificial IntelligenceMachine LearningAnalysisC++AlgorithmsAgileAcademic WritingAPIArduino Software5GArchitecture

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

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