论文标题
通过概率软件建模朝着语义克隆检测
Towards Semantic Clone Detection via Probabilistic Software Modeling
论文作者
论文摘要
语义克隆是具有相似行为的程序组件，但文本表示不同。语义相似性很难检测到，语义克隆检测仍然是一个空旷的问题。我们通过概率软件建模（PSM）介绍语义克隆检测，作为检测语义上等效方法的强大方法。 PSM检查程序的结构和运行时行为，并综合了概率模型（PMS）网络。网络中的每个PM代表程序中的一种方法，并且能够生成和评估运行时事件。我们利用这些功能准确地找到语义克隆。结果表明，该方法可以在完全没有句法相似性的情况下检测语义克隆，并具有高精度和低错误率。
Semantic clones are program components with similar behavior, but different textual representation. Semantic similarity is hard to detect, and semantic clone detection is still an open issue. We present semantic clone detection via Probabilistic Software Modeling (PSM) as a robust method for detecting semantically equivalent methods. PSM inspects the structure and runtime behavior of a program and synthesizes a network of Probabilistic Models (PMs). Each PM in the network represents a method in the program and is capable of generating and evaluating runtime events. We leverage these capabilities to accurately find semantic clones. Results show that the approach can detect semantic clones in the complete absence of syntactic similarity with high precision and low error rates.
