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adversarial-examples

Code for the paper: "Adversarial Examples for Models of Code"

atam

Example programs for ATAM

bottleneck

Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"

c3po

Code for the paper "A Structural Model for Contextual Code Changes"

code2seq

Code for the model presented in the paper: "code2seq: Generating Sequences from Structured Representations of Code"

code2vec

TensorFlow code for the neural network presented in the paper: "code2vec: Learning Distributed Representations of Code"

counting_dimensions

demonstration for our ACL 2018 paper, "On the Practical Computational Power of Finite Precision RNNs for Language Recognition"

differential

differential

elasticsearch

Open Source, Distributed, RESTful Search Engine

esh

statistical similarity of binaries (Esh)

how_attentive_are_gats

Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)

id2vec

layer_norm_expressivity_role

Code for the paper "On the Expressivity Role of LayerNorm in Transformers' Attention" (Findings of ACL'2023)

lstar_extraction

implementation of ICML 2018 paper, Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples

nero

Code and resources for the paper: "Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs"

pigeonjs

Demonstration of the path-extraction process shown in the paper "A General Path-Based Representation for Predicting Program Properties"

pldi-utils

hacks and scripts that are useful for chairing PLDI

prime

rasp

An interpreter for RASP as described in the ICML 2021 paper "Thinking Like Transformers"

rasp-exps

Code for running the transformers in the ICML 2021 paper "Thinking Like Transformers"

rnn_to_prs_cfg

Implementation of TACAS 2021 paper, "Extrapolating CFGs from RNNs"

safe

SAFE static analysis tools

slm-code-generation

TensorFlow code for the neural network presented in the paper: "Structural Language Models of Code" (ICML'2020)

tracy

TRACY

weighted_lstar

implementation for "learning weighted deterministic automata from queries and counterexamples", neurips 2019