Master Thesis: Open-Source Vetting and FOSS Introduction Analysis

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About this opportunity:



Ericsson SW products are developed using Free and Open-Source Software (FOSS). To use these components safely and responsibly, teams need accurate, complete, current, and traceable information about component identity, versions, dependencies, licenses, vulnerabilities, and provenance.

This thesis will investigate how to improve FOSS metadata and build reliable reference data for software vetting and introduction analysis. You will identify common data-quality issues and develop measurable methods to reconcile, enrich, and assess information from multiple sources. The work will consider, compare, how statistical, graph-based, machine-learning, and AI-assisted techniques can support trustworthy decisions without hiding uncertainty or replacing expert review.

What you will do:

• Define a FOSS metadata model and measurable data-quality criteria, such as correctness, completeness, consistency, freshness, and traceability.
• Analyze representative records to identify errors, missing or conflicting information, duplicates, and false positives or negatives.
• Investigate methods for normalizing component identities and versions, resolving records, analyzing dependencies, tracking provenance, and scoring confidence.
• Develop a methodology for creating or maintaining a high-quality reference dataset, including expert review and updates over time.
• Build and evaluate a proof of concept against a baseline using measures such as precision, recall, coverage, confidence, and review effort.
• Provide recommendations for improving automated FOSS vetting and supporting secure, compliant software supply chains.

The skills you bring:

• You are pursuing a Master's degree in Computer Science, Software Engineering, Data Science, Statistics, or a related field.
• You are interested in data quality, analytics, statistics, machine learning, software supply-chain security, or FOSS.
• You are curious about empirical evaluation and how reliable data can support explainable, auditable decisions.
• Experience with Python, SQL, graph data, package ecosystems, Git, CI/CD, or cloud-native software is beneficial, but not required.
• You are analytical, systematic, and able to collaborate on a shared research objective while taking ownership of your contribution.

Why join Ericsson?At Ericsson, you'll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what's possible. To build solutions never seen before to some of the world's toughest problems. You'll be challenged, but you won't be alone. You'll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.

What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more.

Primary country and city: Sweden (SE) || Stockholm

Req ID: 792087