Learning tasks on source code (ie, formal languages) have been considered recently, but
most work has tried to transfer natural language methods and does not capitalize on the …

Natural language elements in source code, eg, the names of variables and functions,
convey useful information. However, most existing bug detection tools ignore this …

Abstract Machine learning-based program analyses have recently shown the promise of
integrating formal and probabilistic reasoning towards aiding software development …

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 …

Neural models of code have shown impressive results when performing tasks such as
predicting method names and identifying certain kinds of bugs. We show that these models …

When working on techniques to address the wide-spread problem of software bugs, one
often faces the need for a large number of realistic bugs in real-world programs. Such bugs …

Static bug detectors are becoming increasingly popular and are widely used by professional
software developers. While most work on bug detectors focuses on whether they find bugs at …

The interest in quantum computing is growing, and with it, the importance of software
platforms to develop quantum programs. Ensuring the correctness of such platforms is …

Deep learning has recently achieved initial success in program analysis tasks such as bug
detection. Lacking real bugs, most existing works construct training and test data by injecting …

Variable names are critical for conveying intended program behavior. Machine learning-
based program analysis methods use variable name representations for a wide range of …