AI 中文总结
该研究针对XR应用性能缺陷修复的技术挑战，提出基于大语言模型的XRFix框架，构建缺陷数据集与定制检测工具，经实验验证其修复性能优于SOTA APR方法。
AI 中文摘要
扩展现实作为一种新兴技术，为终端用户提供了与虚拟和物理环境交互的沉浸式体验。与传统软件不同，XR应用的执行涉及更复杂的计算操作，如3D场景渲染、实时动画和流程模拟。XR应用软件开发过程中的低效编码实践可能会导致各种性能缺陷，降低用户体验，甚至引发晕动症。因此，开发自动化程序修复框架以修复复杂XR程序中的性能缺陷是一项迫切需求。然而，由于存在多项技术挑战，实现这一目标并非易事：（1）缺乏真实世界的XR代码库和缺陷数据集；（2）缺乏准确的缺陷检测工具；（3）缺乏专为XR性能缺陷设计的有效缺陷修复工具。为应对这些挑战，我们提出了一种新颖的基于大语言模型的框架XRFix，用于修复开源XR程序的性能缺陷。我们首先构建了一个领域特定性能缺陷语料库，该语料库基于来自23个开源XR项目的代码库和包含104个真实世界缺陷的XR相关缺陷数据集构建。然后，我们定制了两个静态分析工具，以准确检测C#脚本和资产文件中的缺陷。最后，我们设计了不同的提示词，指导LLMs修复三种不同复杂度缺陷场景中的XR缺陷，即单行级别、函数级别和类级别。我们对五个现成的LLMs进行了广泛实验，以评估XRFix的缺陷修复性能，还将我们的XRFix与三种SOTA APR方法进行了比较。通过静态分析、参考答案比较和人工检查，我们证明XRFix能够有效修复XR缺陷，优于SOTA APR方法。
英文摘要
As an emerging technology, Extended Reality provides end-users with an immersive experience of interacting with virtual and physical environments. Unlike traditional software, the execution of XR applications involves more computationally complex operations, such as 3D scene rendering, real-time animation, and process simulations. Inefficient coding practices during the software development of XR applications may cause various performance bugs, degrading user experience and even causing motion sickness. Thus, it is an urgent need to develop an automated program repair framework for fixing performance bugs in complex XR programs. However, it is non-trivial to achieve this goal due to several technical challenges: (1) a lack of a real-world XR codebase and bug dataset, (2) no accurate bug detection tool, and (3) no effective bug-fixing tool designed for XR performance bugs. To tackle these challenges, we present a novel large language model-based framework, namely XRFix, to repair performance bugs for open-source XR programs. We first construct a corpus of domain-specific performance bugs built with a codebase from 23 open-source XR projects and a dataset of XR-related bugs containing 104 real-world bugs. Then, we tailor two static analysis tools for accurately detecting bugs in both C# scripts and asset files. Last, we design different prompts to instruct LLMs to fix XR bugs in three types of bug scenarios with different complexities, i.e., single-line level, function level, and class level. We conduct extensive experiments on five off-the-shelf LLMs to evaluate the bug-fixing performance of XRFix. We also compare our XRFix with three SOTA APR approaches. Through static analysis, reference answer comparison, and manual inspection, we demonstrate that our XRFix can effectively fix XR bugs, outperforming SOTA APR methods.
Journal refProceedings of the 48th International Conference on Software Engineering (ICSE 2026)
