Master Thesis – AI Assistant for R Code Review
Autoliv GroupOm tjänsten
BACKGROUND
Are you interested in AI, programming and data analysis and want to apply your skills to real-world traffic safety research?
Our Traffic Safety Research team works with in-depth crash databases to answer complex research questions, with results frequently
contributing to scientific publications and conference proceedings. High-quality and accurate data analysis is therefore
essential.
Today, R code used in crash-data analyses is manually reviewed by experienced researchers. The review covers areas such as code
logic, conditions, filters, data joins, calculations and overall analytical flow. While this provides an important quality check,
the process requires considerable manual effort and may not always identify every potential issue.
This Master Thesis explores how Artificial Intelligence and Large Language Models (LLMs) could support and improve this process.
ABOUT THE THESIS
The objective of the project is to develop and validate an AI-based “Coding Buddy” that can review R code used in crash-data
analysis.
Rather than generating new code, the Coding Buddy should act as an intelligent reviewer. Based on the research question, analysis
methodology and available domain knowledge, it should identify potential errors or inconsistencies and provide suggestions for
improvement.
The Coding Buddy could, for example, review:
An important part of the project will be enabling the AI assistant to understand domain-specific crash-data knowledge, such as
database codebooks, coding standards and relevant literature. Technologies such as Retrieval-Augmented Generation (RAG) or similar
AI approaches may therefore be explored.
You will also develop appropriate evaluation metrics and compare the AI-based review with the current manual review process.
Since the project may involve proprietary R code and sensitive crash data, secure data handling will be an important
consideration. Where appropriate, anonymized or synthetic data can be used for development and validation.
RESEARCH QUESTION
To what extent can an AI-based coding assistant improve the quality and efficiency of R code review in crash-data analysis
compared with human review?
YOUR PROJECT
During the thesis, you will:
WHO ARE YOU?
We are looking for 1–2 Master’s students with an interest in combining software development, data analysis and Artificial
Intelligence.
We believe you have:
Knowledge or experience in AI/ML, Large Language Models (LLMs) or RAG is considered an advantage.
Most importantly, you are curious, analytical and motivated by the opportunity to turn emerging AI technologies into a practical
solution for real-world research.
WHAT YOU WILL GAIN
This thesis gives you the opportunity to work at the intersection of Artificial Intelligence, software development and traffic
safety research. You will explore how modern AI techniques can be transformed into a practical industrial application while
working with a real research use case.
INFORMATION AND CONTACT
For more information about the thesis, please contact:
Hanna Jeppsson
Junaid Shaikh
Jawwad Ahmed
Jordanka Kovaceva
More lives saved – more life lived!
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