利用大语言模型为AR应用生成仓库感知的蜕变关系
原标题：Repository-Aware Metamorphic Relation Generation for Augmented Reality Applications using Large Language Models
AI 摘要
该研究提出了一种利用大型语言模型、结合仓库级上下文和推理编排的上下文感知流水线，用于为增强现实（AR）应用自动生成和优化蜕变关系（MRs）。研究在142个移动AR系统仓库上评估，生成了14,916个候选MRs，其中层级上下文覆盖最广（7,004个MRs），并通过代理式审议过程在79.0%的案例中协调冲突，最终88.2%的结果选择了上下文感知关系。人工oracle研究显示精炼后的MRs逻辑有效且可直接转化为测试断言，初步案例研究表明生成的MRs可检测真实代码中的非等价突变。
正文节选
Computer Science > Software Engineering Title:Repository-Aware Metamorphic Relation Generation for Augmented Reality Applications using Large Language Models View PDF HTML (experimental) Abstract:Metamorphic Testing (MT) provides a promising approach for testing software without defined test oracles by specifying expected relations between inputs and outputs, instead of relying on exact outputs. For example, testing Augmented Reality (AR) applications is challenging due to dynamic in