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.

这篇论文提出了一种利用大型语言模型（LLMs）自动识别导致测试失败的代码更改的新方法，并通过定量和定性评估验证了该方法的有效性。通过 EA 开发者报告的问题进行的数据集评估结果显示，我们的方法在准确率上达到了 71％。此外，通过用户研究对我们的模型进行了评估，结果显示在开发者的视角下使用该工具可以显著减少问题调查所需的时间，最高可达 60％。

在游戏开发中利用大型语言模型进行高效的故障分析

Leveraging Large Language Models for Efficient Failure Analysis in Game  Development

Software development teams establish elaborate continuous integration
pipelines containing automated test cases to accelerate the development process
of software. Automated tests help to verify the correctness of code
modifications decreasing the response time to changing requirements. However,
when the software teams do not track the performance impact of pending
modifications, they may need to spend considerable time refactoring existing
code. This paper presents PACE, a program analysis framework that provides
continuous feedback on the performance impact of pending code updates. We
design performance microbenchmarks by mapping the execution time of functional
test cases given a code update. We map microbenchmarks to code stylometry
features and feed them to predictors for performance predictions. Our
experiments achieved significant performance in predicting code performance,
outperforming current state-of-the-art by 75% on neural-represented code
stylometry features.

PACE 是一个程序分析框架，通过映射功能测试用例的执行时间来提供关于挂起代码更新的性能影响的持续反馈，并使用代码风格特征的微基准测试和预测器来实现显著的代码性能预测。