Master thesis: AI-Assisted Nightly Test Result Triage and Fault Analysis

Master thesis investigating how AI-based methods can support engineers by automatically analyzing nightly test failures and assisting with root-cause identification and decision-making in large-scale embedded and automotive software systems. The thesis includes a practical prototype implementation and a scientific evaluation. The exact scope and research direction can be refined together with the students.

Responsibilities

Design, implement, and evaluate an AI-assisted system for automated triage of nightly test failures; Ingest and analyze test logs, reports, metadata, source code, and historical execution results; Classify failures into likely fault categories; Identify probable root-cause locations such as file, module, or function; Produce concise human-readable explanations; Recommend suitable next actions; Conduct a scientific evaluation of the proposed approach

Requirements

Interest in software quality, AI, and developer tooling; Relevant background in Software Engineering, Computer Science, Embedded Systems, DevOps, or AI / Machine Learning

Nice to have

Experience or interest in Python development; ML/LLM application development; Log parsing and data processing; CI/CD systems and automated testing; C/C++ or similar programming languages; Basic understanding of static analysis; Classification metrics and evaluation methods; Automotive embedded systems (meritorious)

Benefits

Full-spectrum development, from SW to HW; In-house production with full insight and control; Workplace flexibility with hybrid work set-up; High-profile clients, global well-known brands; Company health care and health insurance; Flat organization to collaborate and innovate freely