  In games, and more generally in the field of software development, early
detection of bugs is vital to maintain a high quality of the final product.
Automated tests are a powerful tool that can catch a problem earlier in
development by executing periodically. As an example, when new code is
submitted to the code base, a new automated test verifies these changes.
However, identifying the specific change responsible for a test failure becomes
harder when dealing with batches of changes -- especially in the case of a
large-scale project such as a AAA game, where thousands of people contribute to
a single code base. This paper proposes a new approach to automatically
identify which change in the code caused a test to fail. The method leverages
Large Language Models (LLMs) to associate error messages with the corresponding
code changes causing the failure. We investigate the effectiveness of our
approach with quantitative and qualitative evaluations. Our approach reaches an
accuracy of 71% in our newly created dataset, which comprises issues reported
by developers at EA over a period of one year. We further evaluated our model
through a user study to assess the utility and usability of the tool from a
developer perspective, resulting in a significant reduction in time -- up to
60% -- spent investigating issues.
