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Learning semantic program embeddings with graph interval neural network

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Moving Fast with Software Verification

inferredcites →https://doi.org/10.1016/s0169-7439(97)00061-0openalex-snapshot

Introduction to multi-layer feed-forward neural networks

inferredcites →https://doi.org/10.1109/icse.2007.30openalex-snapshot

DECKARD: Scalable and Accurate Tree-Based Detection of Code Clones

inferredcites →https://doi.org/10.1109/icse.2019.00048openalex-snapshot

BugSwarm: Mining and Continuously Growing a Dataset of Reproducible Failures and Fixes

inferredcites →https://doi.org/10.1109/ijcnn.2005.1555942openalex-snapshot

A new model for learning in graph domains

inferredcites →https://doi.org/10.1145/2491411.2491458openalex-snapshot

A statistical semantic language model for source code

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Defects4J: a database of existing faults to enable controlled testing studies for Java programs

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Learning to rank relevant files for bug reports using domain knowledge

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Automatically learning semantic features for defect prediction

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Probabilistic model for code with decision trees

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sk_p: a neural program corrector for MOOCs

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Bugs.jar

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DeepBugs: a learning approach to name-based bug detection

inferredcites →https://doi.org/10.1145/3290353openalex-snapshot

code2vec: learning distributed representations of code

inferredcites →https://doi.org/10.1145/3359591.3359735openalex-snapshot

The adverse effects of code duplication in machine learning models of code

inferredcites →https://doi.org/10.1145/3385412.3385999openalex-snapshot

Blended, precise semantic program embeddings

inferredcites →https://doi.org/10.1145/390013.808479openalex-snapshot

Control flow analysis

inferredcites →https://doi.org/10.1145/800028.808479openalex-snapshot

Control flow analysis

inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-snapshot

Long Short-Term Memory

inferredcites →https://doi.org/10.1609/aaai.v31i1.10742openalex-snapshot

DeepFix: Fixing Common C Language Errors by Deep Learning

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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

inferredcites →https://doi.org/10.3115/v1/p14-1129openalex-snapshot

Fast and Robust Neural Network Joint Models for Statistical Machine Translation

inferredcites →https://doi.org/10.4230/oasics.slate.2021.14openalex-snapshot

Using Machine Learning for Vulnerability Detection and Classification

inferredcites →https://doi.org/10.48550/arxiv.1401.0514openalex-snapshot

Structured Generative Models of Natural Source Code

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Neural Message Passing for Quantum Chemistry

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Attention Is All You Need

inferredcites →https://doi.org/10.48550/arxiv.2005.02161openalex-snapshot

LambdaNet: Probabilistic Type Inference using Graph Neural Networks

inferredcites →https://doi.org/10.5555/2337223.2337322openalex-snapshot

On the naturalness of software

inferredcites →https://doi.org/10.5555/800078.802557openalex-snapshot

Program slicing

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Neural Network-Based Techniques for Automated Code Review and Program Repair: Analysis, Datasets, and Practical Integration Scenarios

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Precise Learning of Source Code Contextual Semantics via Hierarchical Dependence Structure and Graph Attention Networks

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Learning Graph-based Code Representations for Source-level Functional Similarity Detection

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LAMNER

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Perfect is the enemy of test oracle

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Discrete Adversarial Attack to Models of Code

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