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A Comprehensive Evaluation of Large Language Models for Method Name Prediction

https://doi.org/10.2139/ssrn.4886577
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-642-03013-0_14openalex-extracted
Debugging Method Names
inferredcites →https://doi.org/10.1007/s10515-025-00492-xopenalex-extracted
A study on prompt design, advantages and limitations of ChatGPT for deep learning program repair
inferredcites →https://doi.org/10.1007/s10664-015-9369-5openalex-extracted
Automatic identifier inconsistency detection using code dictionary
inferredcites →https://doi.org/10.1016/j.jss.2021.111011openalex-extracted
A hybrid code representation learning approach for predicting method names
inferredcites →https://doi.org/10.1109/apr59189.2023.00012openalex-extracted
An Analysis of the Automatic Bug Fixing Performance of ChatGPT
inferredcites →https://doi.org/10.1109/ase.2019.00062openalex-extracted
Machine Learning Based Recommendation of Method Names: How Far are We
inferredcites →https://doi.org/10.1109/ase.2019.00152openalex-extracted
Retrieve and Refine: Exemplar-Based Neural Comment Generation
inferredcites →https://doi.org/10.1109/ase51524.2021.9678551openalex-extracted
A Mocktail of Source Code Representations
inferredcites →https://doi.org/10.1109/icse43902.2021.00109openalex-extracted
InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees
inferredcites →https://doi.org/10.1109/icse48619.2023.00125openalex-extracted
Impact of Code Language Models on Automated Program Repair
inferredcites →https://doi.org/10.1109/icse48619.2023.00129openalex-extracted
Automated Program Repair in the Era of Large Pre-trained Language Models
inferredcites →https://doi.org/10.1109/qrs60937.2023.00052openalex-extracted
Abstract Syntax Tree for Method Name Prediction: How Far Are We?
inferredcites →https://doi.org/10.1145/3192366.3192412openalex-extracted
A general path-based representation for predicting program properties
inferredcites →https://doi.org/10.1145/3290353openalex-extracted
code2vec: learning distributed representations of code
inferredcites →https://doi.org/10.1145/3294032.3294079openalex-extracted
Method name suggestion with hierarchical attention networks
inferredcites →https://doi.org/10.1145/3324884.3416578openalex-extracted
Retrieve and refine
inferredcites →https://doi.org/10.1145/3377811.3380383openalex-extracted
Retrieval-based neural source code summarization
inferredcites →https://doi.org/10.1145/3377811.3380926openalex-extracted
Suggesting natural method names to check name consistencies
inferredcites →https://doi.org/10.1145/3379597.3387445openalex-extracted
Embedding Java Classes with code2vec
inferredcites →https://doi.org/10.1145/3386252openalex-extracted
Generalizing from a Few Examples
inferredcites →https://doi.org/10.1145/3460319.3464804openalex-extracted
A lightweight framework for function name reassignment based on large-scale stripped binaries
inferredcites →https://doi.org/10.1145/3491101.3519665openalex-extracted
Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models
inferredcites →https://doi.org/10.1145/3501385.3543957openalex-extracted
Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models
inferredcites →https://doi.org/10.1145/3510003.3510140openalex-extracted
Code search based on context-aware code translation
inferredcites →https://doi.org/10.1145/3524610.3527905openalex-extracted
Learning to represent programs with heterogeneous graphs
inferredcites →https://doi.org/10.1145/3533767.3534390openalex-extracted
An extensive study on pre-trained models for program understanding and generation
inferredcites →https://doi.org/10.1145/3545945.3569785openalex-extracted
Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book
inferredcites →https://doi.org/10.1145/3551349.3559555openalex-extracted
Few-shot training LLMs for project-specific code-summarization
inferredcites →https://doi.org/10.1145/3558489.3559072openalex-extracted
Assessing the quality of GitHub copilot’s code generation
inferredcites →https://doi.org/10.1145/3560815openalex-extracted
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
inferredcites →https://doi.org/10.1145/3597503.3623304openalex-extracted
KnowLog: Knowledge Enhanced Pre-trained Language Model for Log Understanding
inferredcites →https://doi.org/10.1145/3597503.3623306openalex-extracted
Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study
inferredcites →https://doi.org/10.1145/3605943openalex-extracted
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey
inferredcites →https://doi.org/10.1145/3611643.3613892openalex-extracted
InferFix: End-to-End Program Repair with LLMs
inferredcites →https://doi.org/10.1145/3631974openalex-extracted
A Survey of Learning-based Automated Program Repair
inferredcites →https://doi.org/10.1145/3641289openalex-extracted
A Survey on Evaluation of Large Language Models
inferredcites →https://doi.org/10.1145/3695988openalex-extracted
Large Language Models for Software Engineering: A Systematic Literature Review
inferredcites →https://doi.org/10.18653/v1/2020.findings-emnlp.139openalex-extracted
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.685openalex-extracted
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
inferredcites →https://doi.org/10.18653/v1/2023.emnlp-main.85openalex-extracted
Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
inferredcites →https://doi.org/10.48550/arxiv.1706.03762openalex-extracted
Attention Is All You Need
inferredcites →https://doi.org/10.48550/arxiv.1810.04805openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1909.09436openalex-extracted
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
inferredcites →https://doi.org/10.48550/arxiv.2002.08155openalex-extracted
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
inferredcites →https://doi.org/10.48550/arxiv.2004.02942openalex-extracted
Embedding Java Classes with code2vec: Improvements from Variable Obfuscation
inferredcites →https://doi.org/10.48550/arxiv.2102.04664openalex-extracted
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
inferredcites →https://doi.org/10.48550/arxiv.2107.03374openalex-extracted
A Direction-Aware Study of LLM-Based Code Translation Across Eight Programming Languages
inferredcites →https://doi.org/10.48550/arxiv.2111.03922openalex-extracted
Automatic Program Repair with OpenAI's Codex: Evaluating QuixBugs
inferredcites →https://doi.org/10.48550/arxiv.2201.11227openalex-extracted
Synchromesh: Reliable code generation from pre-trained language models
inferredcites →https://doi.org/10.48550/arxiv.2203.13474openalex-extracted
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
inferredcites →https://doi.org/10.48550/arxiv.2206.07682openalex-extracted
Emergent Abilities of Large Language Models
inferredcites →https://doi.org/10.48550/arxiv.2207.10397openalex-extracted
CodeT: Code Generation with Generated Tests
inferredcites →https://doi.org/10.48550/arxiv.2304.07327openalex-extracted
OpenAssistant Conversations -- Democratizing Large Language Model Alignment
inferredcites →https://doi.org/10.48550/arxiv.2305.12865openalex-extracted
Automatic Code Summarization via ChatGPT: How Far Are We?
inferredcites →https://doi.org/10.48550/arxiv.2308.12950openalex-extracted
Code Llama: Open Foundation Models for Code
inferredcites →https://doi.org/10.4230/lipics.cosit.2022.18openalex-snapshot
AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.48550/arxiv.1904.05046openalex-snapshot
Generalizing from a Few Examples: A Survey on Few-Shot Learning
inferredcites →https://doi.org/10.48550/arxiv.1910.10419openalex-snapshot
Retrieve and Refine: Exemplar-based Neural Comment Generation
inferredcites →https://doi.org/10.48550/arxiv.2012.07023openalex-snapshot
InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees
inferredcites →https://doi.org/10.48550/arxiv.2308.10620openalex-snapshot
Large Language Models for Software Engineering: A Systematic Literature Review
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