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CodeEditor : Learning to Edit Source Code with Pre-trained Models
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Impact of Peer Code Review on Peer Impression Formation: A Survey
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On Learning Meaningful Code Changes Via Neural Machine Translation
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SkCoder: A Sketch-based Approach for Automatic Code Generation
inferredcites →https://doi.org/10.1109/tse.2020.3020502openalex-snapshot
CODIT: Code Editing With Tree-Based Neural Models
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API code recommendation using statistical learning from fine-grained changes
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Multi-task learning based pre-trained language model for code completion
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Using pre-trained models to boost code review automation
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CrystalBLEU: Precisely and Efficiently Measuring the Similarity of Code
inferredcites →https://doi.org/10.13140/rg.2.2.32525.92648openalex-snapshot
AceCoder: Utilizing Existing Code to Enhance Code Generation
inferredcites →https://doi.org/10.1609/aaai.v34i05.6430openalex-snapshot
TreeGen: A Tree-Based Transformer Architecture for Code Generation
inferredcites →https://doi.org/10.18653/v1/2020.acl-main.703openalex-snapshot
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
inferredcites →https://doi.org/10.18653/v1/2020.findings-emnlp.139openalex-snapshot
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.45openalex-snapshot
Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting
inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.685openalex-snapshot
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
inferredcites →https://doi.org/10.18653/v1/2022.naacl-mainopenalex-snapshot
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
inferredcites →https://doi.org/10.18653/v1/p19-1355openalex-snapshot
Energy and Policy Considerations for Deep Learning in NLP
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.2210.17029openalex-snapshot
Poison Attack and Defense on Deep Source Code Processing Models
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Refactoring Programs Using Large Language Models with Few-Shot Examples
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ZC3: Zero-Shot Cross-Language Code Clone Detection
inferred← citeshttps://doi.org/10.1109/ase63991.2025.00215openalex-snapshot
EfficientEdit: Accelerating Code Editing via Edit-Oriented Speculative Decoding
inferred← citeshttps://doi.org/10.1109/icitcom62788.2024.10762291openalex-snapshot
Machine Learning Innovations in Code Generation: A Systematic Literature Review of Methods, Challenges and Directions
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When Less Is More: Monolingual Fine-Tuning of Language Models for Industrial C# Code Review
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Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study
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Directional Diffusion-Style Code Editing Pre-Training
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Advancing LLM-Generated Code Reliability: A Hybrid Approach for Hallucination Detection
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Poison Attack and Poison Detection on Deep Source Code Processing Models
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CORE: Resolving Code Quality Issues using LLMs
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Improving Automated Code Reviews: Learning From Experience
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CoEdPilot: Recommending Code Edits with Learned Prior Edit Relevance, Project-wise Awareness, and Interactive Nature
inferred← citeshttps://doi.org/10.1145/3674731openalex-snapshot
Automated Commit Intelligence by Pre-training
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Automatically Recommend Code Updates: Are We There Yet?
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Structured Chain-of-Thought Prompting for Code Generation
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Large Language Models for Software Engineering: A Systematic Literature Review
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An Empirical Study of the Non-Determinism of ChatGPT in Code Generation
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FuseApplyBench: Multilingual Benchmark for Trustworthy Code Edit Applying Task
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PATCH : Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing
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ConTested: Consistency-Aided Tested Code Generation with LLM
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SWE-GPT: A Process-Centric Language Model for Automated Software Improvement
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DSCode Comparator: An Interactive Interface for Comparing Models and Evaluating Code for Data Science Tasks
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Leveraging Reviewer Experience in Code Review Comment Generation
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EditFlow: Benchmarking and Optimizing Code Edit Recommendation Systems via Reconstruction of Developer Flows
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Assessing the Capability of LLMs for Deprecated API Usage Updating from Natural Language Descriptions
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No One-Size-Fits-All: Adaptive Code Editing with Feature-Based Strategy Selection
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Aligning User Requirement with Large Language Model in Code Generation
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Hot or Cold? Adaptive Temperature Sampling for Code Generation with Large Language Models
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Self-Edit: Fault-Aware Code Editor for Code Generation
inferred← citeshttps://doi.org/10.3233/faia230317openalex-snapshot
Antecedent Predictions Are More Important Than You Think: An Effective Method for Tree-Based Code Generation
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Do popular apps have issues regarding energy efficiency?
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