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A Database of Real Faults and an Experimental Infrastructure to Enable Controlled Experiments in Software Engineering Research
A curated list of papers, theses, datasets, and tools related to the application of Machine Learning for Software Engineering
Releasing code for "ReCode: Robustness Evaluation of Code Generation Models"
[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
A universal seed scheduler for fuzzers (LibFuzzer and AFL havoc mode) and concolic execution engine (qsym).
ManyTypes4Py: A benchmark Python dataset for machine learning-based type inference
A static analysis library for computing graph representations of Python programs suitable for use with graph neural networks.
The FLARE team's open-source extension to add Python 3 scripting to Ghidra.
Implementation of CCS'2022 paper "SymLM: Predicting Function Names in Stripped Binaries via Context-Sensitive Execution-Aware Code Embeddings"
Guide to using pre-trained large language models of source code
On Training Robust PDF Malware Classifiers (Usenix Security'20) https://arxiv.org/abs/1904.03542
Cost-Aware Robust Tree Ensembles for Security Applications (Usenix Security'21) https://arxiv.org/pdf/1912.01149.pdf
Learning Security Classifiers with Verified Global Robustness Properties (CCS'21) https://arxiv.org/pdf/2105.11363.pdf