Master Thesis: AI-driven Test Failure Root Cause Analysis

Technology, Data & Digital · Data, AI & Analytics · Data Science · Machine Learning · Software Engineering

In short

This Master Thesis project focuses on developing an AI-driven assistant to analyze test failures and identify root causes using Machine Learning and Large Language Models. Utilizing data from CI/CD pipelines and AWS S3, the project aims to improve software quality and delivery efficiency.

Responsibilities

Investigate the use of Machine Learning (ML) and Large Language Models (LLMs) to analyze test failures.
Provide root cause insights across unit, integration, and system testing stages.
Build a Test Failure Root Cause Assistant powered by existing CI test data.
Utilize Large Language Models like LLaMA-3, Mistral, or GPT variants.
Incorporate metadata and features such as commit frequency, test flakiness, smell types, test suite duration, and build environment.
Engineer features across multiple test levels (unit/integration/system).
Train models to classify test failures or recommend root causes.

Requirements

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

Desired Qualifications

Experience with existing CI test data (stored in AWS S3).
Familiarity with Large Language Models like LLaMA-3, Mistral, or GPT variants.
Understanding of metadata & features such as commit frequency, test flakiness, smell types, test suite duration, and build environment.

Benefits

Outstanding opportunity to use your skills and imagination.
Chance to build solutions never seen before.
Be challenged in a team of diverse innovators.
Work towards crafting what comes next.
Join a company that champions diversity and inclusion.

Skills

Artificial IntelligenceMachine LearningLLaMA-3MistralGPTAWS S3PythonGitCI/CDSoftware TestingData AnalysisAlgorithmsAcademic Writing

#AI#Machine Learning#LLM#Software Testing#Root Cause Analysis#CI/CD#AWS S3#Master Thesis

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