Research at the intersection of machine learning, programming languages, and software
engineering has recently taken important steps in proposing learnable probabilistic models …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Static analysis (SA) tools can generate useful static warnings to reveal the problematic code
snippets in a software system without dynamically executing the corresponding source code …

Permissionless blockchains allow the execution of arbitrary programs (called smart
contracts), enabling mutually untrusted entities to interact without relying on trusted third …

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 …

Every programmer has a characteristic style, ranging from preferences about identifier
naming to preferences about object relationships and design patterns. Coding conventions …

Maximum satisfiability (MaxSAT) is an optimization version of SAT that is solved by finding
an optimal truth assignment instead of just a satisfying one. In MaxSAT the objective function …

Imagine a computational process that uses a complex input consisting of multiple" items"(eg,
files, tables, tuples, parameters, configuration rules) The provenance analysis of such a …

Constructing a static call graph requires trade-offs between soundness and precision.
Program analysis techniques for constructing call graphs are unfortunately usually …

Datalog has witnessed promising applications in a variety of domains. We propose a
programming-by-example system, ALPS, to synthesize Datalog programs from input-output …