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Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Siamese Neural Networks: An Overview
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MaxSMT-Based Type Inference for Python 3
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Uncertainty in Artificial Intelligence
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Memorization and Generalization in Neural Code Intelligence Models
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Modeling programs hierarchically with stack-augmented LSTM
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Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
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Vulnerability Detection with Code Language Models: How Far are We?
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Semantic Robustness of Models of Source Code
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Combining Graph-Based Learning With Automated Data Collection for Code Vulnerability Detection
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A Practical Black-Box Attack on Source Code Authorship Identification Classifiers
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Automatic Feature Learning for Predicting Vulnerable Software Components
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SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair
inferredcites →https://doi.org/10.1109/tse.2020.3020502openalex-snapshot
CODIT: Code Editing With Tree-Based Neural Models
inferredcites →https://doi.org/10.1109/tse.2021.3105556openalex-snapshot
Modeling Functional Similarity in Source Code With Graph-Based Siamese Networks
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Competition-level code generation with AlphaCode
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Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering
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Probabilistic model for code with decision trees
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Proceedings of the 26th International Conference on Compiler Construction
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Deep learning similarities from different representations of source code
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A Survey of Machine Learning for Big Code and Naturalness
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DeepBugs: a learning approach to name-based bug detection
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Improving bug detection via context-based code representation learning and attention-based neural networks
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AutoPandas: neural-backed generators for program synthesis
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Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning
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Adversarial Attacks on Deep-learning Models in Natural Language Processing
inferredcites →https://doi.org/10.1145/3379597.3387449openalex-snapshot
Improved Automatic Summarization of Subroutines via Attention to File Context
inferredcites →https://doi.org/10.1145/3387904.3389268openalex-snapshot
Improved Code Summarization via a Graph Neural Network
inferredcites →https://doi.org/10.1145/3411764openalex-snapshot
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
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Flow2Vec: value-flow-based precise code embedding
inferredcites →https://doi.org/10.1145/3429444openalex-snapshot
Interpreting Deep Learning-based Vulnerability Detector Predictions Based on Heuristic Searching
inferredcites →https://doi.org/10.1145/3447571openalex-snapshot
Deep Graph Matching and Searching for Semantic Code Retrieval
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A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
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Adversarial Robustness of Deep Code Comment Generation
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A Survey on Deep Learning for Software Engineering
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On the effectiveness of pretrained models for API learning
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On the transferability of pre-trained language models for low-resource programming languages
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An exploratory study on code attention in BERT
inferredcites →https://doi.org/10.1145/3580305.3599790openalex-snapshot
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X
inferredcites →https://doi.org/10.1145/3715908openalex-snapshot
Large Language Model-Aware In-Context Learning for Code Generation
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Heterogeneous information networks
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ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages
inferredcites →https://doi.org/10.18653/v1/w18-25openalex-snapshot
Proceedings of Workshop for NLP Open Source Software (NLP-OSS)
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Using Machine Learning for Vulnerability Detection and Classification
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Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks
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Latent Attention For If-Then Program Synthesis
inferredcites →https://doi.org/10.48550/arxiv.1705.07867openalex-snapshot
SmartPaste: Learning to Adapt Source Code
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Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
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Deep Reinforcement Learning for Programming Language Correction
inferredcites →https://doi.org/10.48550/arxiv.1812.07170openalex-snapshot
Learning to Generate Corrective Patches using Neural Machine Translation
inferredcites →https://doi.org/10.48550/arxiv.1902.06705openalex-snapshot
On Evaluating Adversarial Robustness
inferredcites →https://doi.org/10.48550/arxiv.1909.09436openalex-snapshot
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
inferredcites →https://doi.org/10.48550/arxiv.1910.10683openalex-snapshot
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
inferredcites →https://doi.org/10.48550/arxiv.2003.11768openalex-snapshot
On-the-Fly Adaptation of Source Code Models using Meta-Learning
inferredcites →https://doi.org/10.48550/arxiv.2004.00348openalex-snapshot
OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints
inferredcites →https://doi.org/10.48550/arxiv.2008.05515openalex-snapshot
Synergy between Machine/Deep Learning and Software Engineering: How Far Are We?
inferredcites →https://doi.org/10.48550/arxiv.2009.06520openalex-snapshot
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
inferredcites →https://doi.org/10.48550/arxiv.2009.08525openalex-snapshot
Deep Learning & Software Engineering: State of Research and Future Directions
inferredcites →https://doi.org/10.48550/arxiv.2108.04556openalex-snapshot
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation
inferredcites →https://doi.org/10.48550/arxiv.2202.07646openalex-snapshot
Quantifying Memorization Across Neural Language Models
inferredcites →https://doi.org/10.48550/arxiv.2305.06161openalex-snapshot
StarCoder: may the source be with you!
inferredcites →https://doi.org/10.48550/arxiv.2305.08360openalex-snapshot
Improving ChatGPT Prompt for Code Generation
inferredcites →https://doi.org/10.48550/arxiv.2306.08568openalex-snapshot
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
inferredcites →https://doi.org/10.48550/arxiv.2308.01861openalex-snapshot
ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation
inferredcites →https://doi.org/10.48550/arxiv.2308.12950openalex-snapshot
Code Llama: Open Foundation Models for Code
inferredcites →https://doi.org/10.52202/075280-2837openalex-snapshot
Grammar Prompting for Domain-Specific Language Generation with Large Language Models
inferredcites →https://doi.org/10.5281/zenodo.7908467openalex-snapshot
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Syn2Vec: A Semantic‐Aware Initialization Method for AST Path–Based Code Representation
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Software maintainability prediction using static code metrics and machine learning
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BMGANet: A deep learning model for source code vulnerability detection by integrating token-level and function-level features
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A review of backdoor attacks and defenses in code large language models: Implications for security measures
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Artificial intelligence for source code understanding tasks: A systematic mapping study
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SLMFL: Structural and semantic fusion for fault localization based on Small Language Models
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Integrating a ChatGPT-enhanced error feedback system with epistemic network analysis to explore students’ learning programming behaviors
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On the Effectiveness of Automatic Code Generation for Synthetic Dataset Creation
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Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review
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User Feedback-Driven Generative Models-Based Methodology for Service Construction
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Intelligent Traceability to Support Software Maintainability and Accountability
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How to Select Pre-Trained Code Models for Reuse? A Learning Perspective
inferred← citeshttps://doi.org/10.1109/tse.2024.3524461openalex-snapshot
Understanding the Robustness of Transformer-Based Code Intelligence via Code Transformation: Challenges and Opportunities
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Can Large Language Models Serve as Evaluators for Code Summarization?
inferred← citeshttps://doi.org/10.1145/3696410.3714889openalex-snapshot
WebCode2M: A Real-World Dataset for Code Generation from Webpage Designs
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LaTCoder: Converting Webpage Design to Code with Layout-as-Thought
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Machine Learning for Blockchain Data Analysis: Progress and Opportunities
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Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach
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Large Language Models for API Classification: An Explorative Study
inferred← citeshttps://doi.org/10.1145/3757915openalex-snapshot
Fault Localization from the Semantic Code Search Perspective
inferred← citeshttps://doi.org/10.1145/3786582.3786818openalex-snapshot
Setup AGent (SAG): A Dual-Model LLM Agent for Autonomous End-to-End Java Project Configuration
inferred← citeshttps://doi.org/10.1145/3786771openalex-snapshot
Assessing and Advancing Benchmarks for Evaluating Large Language Models in Software Engineering Tasks
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Beyond Language Boundaries: Uncovering Programming Language Families for Code Language Models
inferred← citeshttps://doi.org/10.1145/3797261openalex-snapshot
Towards an Understanding of Context Utilization in Code Intelligence
inferred← citeshttps://doi.org/10.1145/3813804openalex-snapshot
HumanEval-V: Systematic Evaluation of Visual Reasoning in Large Multimodal Models for Code Generation
inferred← citeshttps://doi.org/10.1145/3820377openalex-snapshot
Debugging Functionality-Twisting Translations by LLMs via Differential Testing with Bayesian Prior
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STALL + : Boosting LLM-based Repository-level Code Completion with Static Analysis
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Detection of GenAI-produced and student-written C# code: A comparative study of classifier algorithms and code stylometry features
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Challenges and Paths Towards AI for Software Engineering
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GeoJSEval: An Automated Evaluation Framework for Large Language Models on JavaScript-Based Geospatial Computation and Visualization Code Generation
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A Survey of Big Data Approaches in Natural Language Formation and Computing Science
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A Comparison Between Traditional Software Engineering Practices and AI-Driven Methodologies
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