Actionable Warning Identification (AWI) plays a crucial role in improving the usability of static
code analyzers. With recent advances in Machine Learning (ML), various approaches have …

Type inference over partial contexts in dynamically typed languages is challenging. In this
work, we present a graph neural network model that predicts types by probabilistically …

Static analysis tools have showcased their importance and usefulness in automated
detection of defects. However, the tools are known to generate a large number of alarms …

Similar software vulnerabilities recur because developers reuse existing vulnerable code, or
make similar mistakes when implementing the same logic. Recently, various analysis …

We propose a general end-to-end deep learning framework Code2Inv, which takes a
verification task and a proof checker as input, and automatically learns a valid proof for the …

Static bug detectors aim at helping developers to automatically find and prevent bugs. In this
experience paper, we study the effectiveness of static bug detectors at identifying Null …

Software APIs exhibit rich diversity and complexity which not only renders them a common
source of programming errors but also hinders program analysis tools for checking them …

We present BayeSmith, a general framework for automatically learning probabilistic models
of static analysis alarms. Several probabilistic reasoning techniques have recently been …

Static checker frameworks support software developers by automatically discovering bugs
that fit general-purpose bug patterns. These frameworks ship with hundreds of detectors for …

The two broad approaches to discover properties of programs---static and dynamic analyses-
--have complementary strengths: static techniques perform exhaustive exploration and …