Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

Bosch R&D Center Lund stands for modern development in cutting edge technology in the areas of connectivity, security, mobility solutions and AI.  We are growing rapidly and looking for people to join us on our mission to become the Bosch Group’s 1st address for secure connected mobility solutions.  We are working on a range of interesting projects, with a particular focus on software development for the automotive industry, electrical bicycles and Internet of Things. 

Job Description

Problem statement

In systems of high integrity, it’s imperative that Cybersecurity requirements of the system are realized and verified through out the life cycle of the product. We want to explore and understand if the use of Canary Elements can be valid indicators verifying the integrity of the AI (LLM) system used for generating requirements and/or code. Also the in case not using AI for generating artifacts is of interest to be studied.

The systems in scope are embedded systems and not IT systems.

Proposed solution

This Master Thesis investigates the application of canary elements methodologies as a validation mechanism for AI-generated software artifacts, specifically targeting code generation and requirements specification in domains with stringent cybersecurity constraints.

The research explores how strategically embedded keywords, markers, or patterns (canary elements) can serve as verification checkpoints to detect hallucinations, ensure compliance with domain-specific constraints, and validate the correctness of AI-generated outputs (or any generated output). This approach is particularly crucial in safety-critical systems (automotive, aerospace, medical devices) and security-sensitive applications where AI-generated code must adhere to strict standards such as ISO 21434, ISO26262 or Common Criteria. In this thesis we focus on the Cybersecurity domain of an embedded system.

Research Objectives

Conduct a systematic literature review of existing canary code techniques and AI output validation methodsAnalyze current approaches for ensuring reliability in AI-generated software artifactsDevelop a taxonomy of canary code patterns applicable to different software engineering contextsDesign and implement a proof-of-concept validation framework using a state-of-the-art Large Language ModelEvaluate the effectiveness of canary-based validation in detecting AI-generated errors and compliance violations

Outcomes/Deliverables

Comprehensive survey of existing validation techniquesNovel canary code methodology tailored for AI-generated softwareWorking prototype demonstrating the approach with measurable validation metricsGuidelines for implementing canary-based validation in safety/security-critical development workflows

This research contributes to the emerging field of trustworthy AI in software engineering, addressing critical gaps in ensuring the reliability of AI-assisted development tools.

You will of course have the opportunity to shape the thesis based on your knowledge, skills and discoveries during the project.

Scope of master thesis project

Two students completing 30 credits each (20 weeks) onsite at the Lund office

 

Qualifications

Your profile

In order to be successful in the project with think you are:

Student in Information Technology, Computer Science, Electronics, Math or Physics.Required knowledge / courses on data science, cybersecurity and AIExperienced with or have at least some knowledge of programming in Python, C++ or similar.Self-driven, able to challenge yourself, and gain the experience needed to move the project forward.A person with team spirit, social skills and a curiosity for exploring new technology areas.

Additional Information

Why choose Bosch:

In 2022, for the third year in a row, Bosch have received the "World's Best Employer" award from Forbes/Statista, ranking us among the top 3% of the world's most attractive employers.

At Bosch we believe that diversity is our strength. We look at diversity in gender, generation, nationalities, and culture as our advantage. We believe mixed teams to be more successful because they utilize the potential offered by different perspectives and solution strategies. We therefore promote mixed teams at all levels and draw on the entire talent pool.