Master Thesis – AI Assistant for R Code Review
A quick look here can change lives!
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:
Code flow and logic
Data joins and merging
Filters and conditions
Calculations
Missing or incorrect values
Differences between expected and actual analytical outputs
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:
Develop an AI-based Coding Buddy for reviewing R code
Define a structured analysis specification and error taxonomy
Build a labelled benchmark dataset of R scripts
Use synthetic data and intentionally introduced coding errors for development and testing
Explore how domain-specific knowledge can be incorporated into the AI solution
Evaluate the Coding Buddy against manual code review
Measure its ability to identify errors and reduce manual review effort
Identify and describe the strengths and limitations of the solution
Deliver a working prototype, thesis report and final presentation
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:
Experience with R programming
Experience with Python
An interest in AI, machine learning and data analysis
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
hanna.jeppsson@autoliv.com
Junaid Shaikh
junaid.shaikh@autoliv.com
Jawwad Ahmed
jawwad.ahmed@autoliv.com
Jordanka Kovaceva
jordanka.kovaceva@chalmers.se
More lives saved – more life lived!
- Function
- Engineering, Development & Research
- Location
- Autoliv Research - Göteborg
Autoliv Research - Göteborg
Outstanding People Embracing Change
Our position as a worldwide leader in automotive safety systems, saving 37,000 lives every year, is achieved by our approximately 65,000 outstanding colleagues around the world. Their diverse assets, breadth and depth of expertise and desire to develop, in combination with Autoliv’s ambition to enable fulfilling careers, supports our vision of Saving More Lives.
About Autoliv Sweden
Autoliv is the worldwide leader in automotive safety systems. Through our group companies, we develop, manufacture and market protective systems, such as airbags, seatbelts, and steering wheels for all major automotive manufacturers in the world as well as mobility safety solutions.
At Autoliv, we challenge and redefine the standards of mobility safety to sustainably deliver leading solutions. In 2025, our products saved approximately 40,000 lives and reduced around 600,000 injuries.
Our ~64,000 colleagues in 25 countries are passionate about our vision of Saving More Lives and quality is at the heart of everything we do. We drive innovation, research, and development at our 13 technical centers, with their 20 test tracks.