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

Job description

Background Modern software development relies heavily on continuous integration and automated testing to ensure software quality and system stability. In large-scale embedded and automotive software systems, nightly test executions may generate thousands of test results across multiple platforms, configurations, and environments. A significant challenge for engineering teams is the manual triage of failing tests. Engineers often need to inspect logs, test reports, metadata, source code, and historical execution results to determine whether a failure originates from production code, unstable tests, infrastructure issues, or configuration problems. This process is both time-consuming and difficult to scale. Recent advances in Artificial Intelligence, Machine Learning, and Large Language Models (LLMs) open new possibilities for intelligent analysis of software testing artifacts and automated fault triage. However, the practical usefulness, robustness, and limitations of such systems in real-world CI environments remain largely unexplored. This thesis aims to investigate how AI-based methods can support engineers by automatically analyzing nightly test failures and assisting with root-...