在一个多语言AAA视频游戏项目中预测有缺陷的可视化代码变更
Predicting Defective Visual Code Changes in a Multi-Language AAA Video Game Project
AI总结：
本文针对AAA游戏项目中可视化代码缺陷较多的问题，构建了包含可视化代码指标的缺陷预测模型，发现纳入可视化代码特征能显著提升AUC和MCC等预测性能。
摘要：
视频游戏开发越来越依赖使用可视化编程语言作为构建视频游戏功能的主要方式。使用可视化编程的目的是将游戏逻辑交到游戏设计师手中，他们可能不太精通文本编程。在本文中，我们经验性地观察到，在一个AAA视频游戏项目代码库中，包含可视化代码的缺陷诱发提交比包含文本代码的要多。这表明现有的文本代码即时（JIT）缺陷预测模型可能无效，因为它们没有考虑可视化代码的变更，Electronic Arts (EA) 正在对这些模型进行评估。因此，我们将研究重点放在构建包含可视化代码指标的可视化代码缺陷预测模型上，并使用语言无关特征和文本代码指标的缺陷预测模型来评估这些模型。我们使用从一个AAA视频游戏项目历史代码库中提取的特征，以及70个使用文本和可视化代码的开源项目的历史代码库来测试我们的模型。我们发现，对于包含可视化代码提交多于文本代码的项目，当纳入可视化代码特征时，缺陷预测模型在ROC曲线下面积（AUC）和Mathews相关系数（MCC）方面总体上具有更好的性能。
英文摘要：
Video game development increasingly relies on using visual programming languages as the primary way to build video game features. The aim of using visual programming is to move game logic into the hands of game designers, who may not be as well versed in textual coding. In this paper, we empirically observe that there are more defect-inducing commits containing visual code than textual code in a AAA video game project codebase. This indicates that the existing textual code Just-in-Time (JIT) defect prediction models under evaluation by Electronic Arts (EA) may be ineffective as they do not account for changes in visual code. Thus, we focus our research on constructing visual code defect prediction models that encompass visual code metrics and evaluate the models against defect prediction models that use language agnostic features, and textual code metrics. We test our models using features extracted from the historical codebase of a AAA video game project, as well as the historical codebases of 70 open source projects that use textual and visual code. We find that defect prediction models have better performance overall in terms of the area under the ROC curve (AUC), and Mathews Correlation Coefficient (MCC) when incorporating visual code features for projects that contain more commits with visual code than textual code.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
