论文标题
红外：元错误检测器
Infrared: A Meta Bug Detector
论文作者
论文摘要
深度学习方法的最新突破引发了人们对基于学习的错误探测器的兴趣。与传统的静态分析工具相比，这些错误检测器是直接从数据中学到的，因此更容易创建。另一方面，它们很难训练，需要大量数据，而这些数据不容易获得。在本文中，我们提出了一种称为Meta错误检测的新方法，该方法比现有基于学习的错误检测器具有三个至关重要的优势：错误类型（即能够捕获培训中完全无法观察到的错误类型），自我解释的方法（即能够进行较少的训练），而不是进行任何规定的数据，并且有效地进行了较少的训练（即有效地训练）（即有效地训练）（即有效地）（即有效）（即有效的）（即有效）（即，都要有效地进行较高的训练（即）。我们的广泛评估表明，我们的元错误检测器（MBD）有效地捕获了各种错误，包括NULL指针解除，阵列索引外部漏洞，文件句柄泄漏，甚至是并发程序中的数据竞赛；在此过程中，MBD还大大优于几个值得注意的基线，包括Facebook Chell，一种著名的静态分析工具和最新的异常检测方法FICS。
The recent breakthroughs in deep learning methods have sparked a wave of interest in learning-based bug detectors. Compared to the traditional static analysis tools, these bug detectors are directly learned from data, thus, easier to create. On the other hand, they are difficult to train, requiring a large amount of data which is not readily available. In this paper, we propose a new approach, called meta bug detection, which offers three crucial advantages over existing learning-based bug detectors: bug-type generic (i.e., capable of catching the types of bugs that are totally unobserved during training), self-explainable (i.e., capable of explaining its own prediction without any external interpretability methods) and sample efficient (i.e., requiring substantially less training data than standard bug detectors). Our extensive evaluation shows our meta bug detector (MBD) is effective in catching a variety of bugs including null pointer dereference, array index out-of-bound, file handle leak, and even data races in concurrent programs; in the process MBD also significantly outperforms several noteworthy baselines including Facebook Infer, a prominent static analysis tool, and FICS, the latest anomaly detection method.
