Graph-based code representations such as Code Property Graphs achieve the highest accuracy (average 82.6%) in predicting patch correctness across 15 benchmarks and outperform sequence and tree representations when used with GNN classifiers.
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
abstract
Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks. The only underlying LSTM structure that has been explored so far is a linear chain. However, natural language exhibits syntactic properties that would naturally combine words to phrases. We introduce the Tree-LSTM, a generalization of LSTMs to tree-structured network topologies. Tree-LSTMs outperform all existing systems and strong LSTM baselines on two tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task 1) and sentiment classification (Stanford Sentiment Treebank).
verdicts
UNVERDICTED 5representative citing papers
Coda is an end-to-end neural decompiler that recovers source code from binaries at 82% accuracy on unseen samples where conventional tools achieve 0%.
CodeBERT pre-trains a bimodal model on code and text pairs plus unimodal data to achieve state-of-the-art results on natural language code search and code documentation generation.
PR-LSTM replaces linear recurrence with recursive gated merging over a balanced binary tree to achieve log-depth parallelism without restricting transitions to linear or associative forms.
Describes a multilevel streaming text analytics framework combining Spark streaming, LSTM models, and SQL processing for real-time sentiment analysis demonstrated on a business use case.
citing papers explorer
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On the Effectiveness of Code Representation in Deep Learning-Based Automated Patch Correctness Assessment
Graph-based code representations such as Code Property Graphs achieve the highest accuracy (average 82.6%) in predicting patch correctness across 15 benchmarks and outperform sequence and tree representations when used with GNN classifiers.
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A Neural-based Program Decompiler
Coda is an end-to-end neural decompiler that recovers source code from binaries at 82% accuracy on unseen samples where conventional tools achieve 0%.
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages
CodeBERT pre-trains a bimodal model on code and text pairs plus unimodal data to achieve state-of-the-art results on natural language code search and code documentation generation.
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Parallel Recursive LSTM
PR-LSTM replaces linear recurrence with recursive gated merging over a balanced binary tree to achieve log-depth parallelism without restricting transitions to linear or associative forms.
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A Scalable Framework for Multilevel Streaming Data Analytics using Deep Learning
Describes a multilevel streaming text analytics framework combining Spark streaming, LSTM models, and SQL processing for real-time sentiment analysis demonstrated on a business use case.