https://github.com/tthtlc/awesome-source-analysis
Source code understanding via Machine Learning techniques
https://github.com/tthtlc/awesome-source-analysis
List: awesome-source-analysis
automated-programming deep-learning machine-learning source-code-analysis
Last synced: about 1 month ago
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Source code understanding via Machine Learning techniques
- Host: GitHub
- URL: https://github.com/tthtlc/awesome-source-analysis
- Owner: tthtlc
- Created: 2018-06-19T13:44:19.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2022-11-29T06:51:01.000Z (over 3 years ago)
- Last Synced: 2025-04-07T00:16:29.019Z (about 1 year ago)
- Topics: automated-programming, deep-learning, machine-learning, source-code-analysis
- Homepage:
- Size: 75.2 KB
- Stars: 136
- Watchers: 7
- Forks: 25
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this 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](https://github.com/tthtlc)
## Contributing
Please feel free to [pull requests](https://github.com/tthtlc/awesome-source-analysis/pulls), email Peter Teoh (htmldeveloper@gmail.com) or join our chats to add links.
[[Join the chat at https://gitter.im/tthtlc/awesome-source-analysis](https://gitter.im/tthtlc/awesome-source-analysis)]
## Sharing
## Table of Contents
Machine-Learning-Guided Selectively Unsound Static Analysis
http://www.seas.upenn.edu/~kheo/home/paper/icse17-heohyi.pdf
A Survey of Machine Learning for Big Code and Naturalness
https://arxiv.org/pdf/1709.06182
Ariadne: Analysis for Machine Learning Programs
https://arxiv.org/pdf/1805.04058
The use of machine learning with signal- and NLP processing of source code to fingerprint, detect, and classify vulnerabilities and weaknesses with MARFCAT
https://arxiv.org/abs/1010.2511
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
https://arxiv.org/pdf/1801.01681
code2vec: Learning Distributed Representations of Code
https://arxiv.org/pdf/1803.09473
Automated software vulnerability detection with machine learning
https://arxiv.org/abs/1803.04497
Automatic feature learning for vulnerability prediction
https://arxiv.org/pdf/1708.02368
Neural Turing Machines
https://arxiv.org/pdf/1410.5401.pdf
DeepCoder: Learning to Write Programs
https://arxiv.org/abs/1611.01989
Recent Advances in Neural Program Synthesis
https://arxiv.org/pdf/1802.02353
Neural-Guided Deductive Search for Real-Time Program Synthesis
https://arxiv.org/pdf/1804.01186
RobustFill: Neural Program Learning under Noisy I/O
https://arxiv.org/pdf/1703.07469
On End-to-End Program Generation from User Intention by Deep
https://arxiv.org/pdf/1510.07211
Neural Program Search: Solving Programming Tasks from Description
https://arxiv.org/pdf/1802.04335
A Syntactic Neural Model for General-Purpose Code Generation
https://arxiv.org/pdf/1704.01696
Building Machines That Learn and Think Like People
https://arxiv.org/pdf/1604.00289
Differentiable Programs with Neural Libraries
https://arxiv.org/pdf/1611.02109
Summary-TerpreT: A Probabilistic Programming Language for Program Induction
https://arxiv.org/pdf/1612.00817
Auto-Documenation for Software Development
https://arxiv.org/pdf/1701.08485
BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement Learning
https://arxiv.org/pdf/1709.01308
Boda-RTC: Productive Generation of Portable, Efficient Code ...
https://arxiv.org/pdf/1606.00094
Making Neural Programming Architectures Generalize via Recursion
https://arxiv.org/pdf/1704.06611
Differentiable Functional Program Interpreters
https://arxiv.org/pdf/1611.01988
Utilizing Static Analysis and Code Generation to Accelerate
https://arxiv.org/pdf/1206.6466
Deep Probabilistic Programming Languages: A Qualitative Study
https://arxiv.org/pdf/1804.06458
BinPro: A Tool for Binary Source Code Provenance
https://arxiv.org/pdf/1711.00830
A Survey on Compiler Autotuning using Machine Learning
https://arxiv.org/pdf/1801.04405
Estimating defectiveness of source code: A predictive model using GitHub content
https://arxiv.org/pdf/1803.07764
EMBER: An Open Dataset for Training Static PE Malware Machine
https://arxiv.org/pdf/1804.04637
On End-to-End Program Generation from User Intention by Deep Neural Networks
https://arxiv.org/pdf/1510.07211
Utilizing Static Analysis and Code Generation to Accelerate Neural Networks
https://arxiv.org/abs/1206.6466
DLPaper2Code: Auto-generation of Code from Deep Learning Research Paper
https://arxiv.org/pdf/1711.03543
Inferring Generative Model Structure with Static Analysis
https://arxiv.org/pdf/1709.02477
Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities
https://arxiv.org/pdf/1707.04742
DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks
https://arxiv.org/pdf/1711.09666
Automatic Structure Discovery for Large Source Code
https://arxiv.org/pdf/1202.3335
Comment Generation for Source Code: Survey
https://arxiv.org/pdf/1802.02971
Towards Reverse-Engineering Black-Box Neural Networks
https://arxiv.org/abs/1711.01768
Database Reverse Engineering based on Association Rule Mining
https://arxiv.org/pdf/1004.3272.pdf
Automated detection and classification of cryptographic algorithms in binary programs through machine learning
https://arxiv.org/pdf/1503.01186
Automatically Generating Commit Messages from Diffs using Neural Machine Translation
https://arxiv.org/pdf/1708.09492
When Coding Style Survives Compilation: De-anonymizing Programmers from Executable
https://arxiv.org/pdf/1512.08546
Code smells
https://arxiv.org/pdf/1802.06063
Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains
https://arxiv.org/pdf/1703.07909
pix2code: Generating Code from a Graphical User Interface Screenshot
https://arxiv.org/pdf/1705.07962
Deep Learning in Software Engineering
https://arxiv.org/pdf/1805.04825
Predicting Software Defects Through SVM: An Empirical Approach
https://arxiv.org/pdf/1803.03220
A Survey of Reverse Engineering and Program Comprehension
https://arxiv.org/pdf/cs/0503068
https://owasp.org/www-project-top-ten/2017/
https://arxiv.org/pdf/1709.07101.pdf
https://arxiv.org/pdf/1805.05206.pdf
https://arxiv.org/pdf/1807.09160.pdf
https://arxiv.org/pdf/1806.07336.pdf
Or just search arxiv.org (inaccuracies in identifying papers expected): [recent arxiv.org search](/summary_6dec2018.md)
[LLVM based vulnerabilities search](/summary_llvm_source6dec2018.md)
As an extension
https://ml4code.github.io/
(this site being an offshoot of the paper: https://arxiv.org/abs/1709.06182)