Awesome Source Analysis Save

Source code understanding via Machine Learning techniques

Project README

Awesome Source Code Analysis Via Machine Learning Techniques

A list of resources for source code analysis application using Machine Learning techniques (eg, Deep Learning, PCA, SVM, Bayesian, proabilistic models, reinformcement learning techniques etc)

Maintainers - Peter Teoh


Please feel free to pull requests, email Peter Teoh ([email protected]) or join our chats to add links.

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Table of Contents

Machine-Learning-Guided Selectively Unsound Static Analysis

A Survey of Machine Learning for Big Code and Naturalness

Ariadne: Analysis for Machine Learning Programs

The use of machine learning with signal- and NLP processing of source code to fingerprint, detect, and classify vulnerabilities and weaknesses with MARFCAT

VulDeePecker: A Deep Learning-Based System for Vulnerability Detection

code2vec: Learning Distributed Representations of Code

Automated software vulnerability detection with machine learning

Automatic feature learning for vulnerability prediction

Neural Turing Machines

DeepCoder: Learning to Write Programs

Recent Advances in Neural Program Synthesis

Neural-Guided Deductive Search for Real-Time Program Synthesis

RobustFill: Neural Program Learning under Noisy I/O

On End-to-End Program Generation from User Intention by Deep

Neural Program Search: Solving Programming Tasks from Description

A Syntactic Neural Model for General-Purpose Code Generation

Building Machines That Learn and Think Like People

Differentiable Programs with Neural Libraries

Summary-TerpreT: A Probabilistic Programming Language for Program Induction

Auto-Documenation for Software Development

BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning

Boda-RTC: Productive Generation of Portable, Efficient Code ...

Making Neural Programming Architectures Generalize via Recursion

Differentiable Functional Program Interpreters

Utilizing Static Analysis and Code Generation to Accelerate

Deep Probabilistic Programming Languages: A Qualitative Study

BinPro: A Tool for Binary Source Code Provenance

A Survey on Compiler Autotuning using Machine Learning

Estimating defectiveness of source code: A predictive model using GitHub content

EMBER: An Open Dataset for Training Static PE Malware Machine

On End-to-End Program Generation from User Intention by Deep Neural Networks

Utilizing Static Analysis and Code Generation to Accelerate Neural Networks

DLPaper2Code: Auto-generation of Code from Deep Learning Research Paper

Inferring Generative Model Structure with Static Analysis

Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities

DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks

Automatic Structure Discovery for Large Source Code

Comment Generation for Source Code: Survey

Towards Reverse-Engineering Black-Box Neural Networks

Database Reverse Engineering based on Association Rule Mining

Automated detection and classification of cryptographic algorithms in binary programs through machine learning

Automatically Generating Commit Messages from Diffs using Neural Machine Translation

When Coding Style Survives Compilation: De-anonymizing Programmers from Executable

Code smells

Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains

pix2code: Generating Code from a Graphical User Interface Screenshot

Deep Learning in Software Engineering

Predicting Software Defects Through SVM: An Empirical Approach

A Survey of Reverse Engineering and Program Comprehension

Or just search (inaccuracies in identifying papers expected): recent search

LLVM based vulnerabilities search

As an extension

(this site being an offshoot of the paper:

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