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PTM-APIRec: Leveraging Pre-trained Models of Source Code in API Recommendation

https://doi.org/10.1145/3632745
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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-642-03013-0_15openalex-snapshot
MAPO: Mining and Recommending API Usage Patterns
inferredcites →https://doi.org/10.1007/s10664-021-10040-2openalex-snapshot
“More Than Deep Learning”: post-processing for API sequence recommendation
inferredcites →https://doi.org/10.1016/j.jss.2023.111786openalex-snapshot
ARist: An effective API argument recommendation approach
inferredcites →https://doi.org/10.1109/ase.2015.109openalex-snapshot
Recommending API Usages for Mobile Apps with Hidden Markov Model
inferredcites →https://doi.org/10.1109/ase.2019.00099openalex-snapshot
CLCDSA: Cross Language Code Clone Detection using Syntactical Features and API Documentation
inferredcites →https://doi.org/10.1109/icse-nier.2019.00016openalex-snapshot
Leveraging Small Software Engineering Data Sets with Pre-Trained Neural Networks
inferredcites →https://doi.org/10.1109/icse.2012.6227135openalex-snapshot
On the naturalness of software
inferredcites →https://doi.org/10.1109/icse.2012.6227136openalex-snapshot
Automatic parameter recommendation for practical API usage
inferredcites →https://doi.org/10.1109/icse.2012.6227205openalex-snapshot
Graph-based pattern-oriented, context-sensitive source code completion
inferredcites →https://doi.org/10.1109/icse.2015.336openalex-snapshot
Graph-Based Statistical Language Model for Code
inferredcites →https://doi.org/10.1109/icse.2019.00109openalex-snapshot
FOCUS: A Recommender System for Mining API Function Calls and Usage Patterns
inferredcites →https://doi.org/10.1109/icsm.2015.7332481openalex-snapshot
PARC: Recommending API methods parameters
inferredcites →https://doi.org/10.1109/msr.2013.6624029openalex-snapshot
Mining source code repositories at massive scale using language modeling
inferredcites →https://doi.org/10.1109/msr.2013.6624045openalex-snapshot
Mining succinct and high-coverage API usage patterns from source code
inferredcites →https://doi.org/10.1109/saner.2016.80openalex-snapshot
RACK: Automatic API Recommendation Using Crowdsourced Knowledge
inferredcites →https://doi.org/10.1109/tse.2021.3053111openalex-snapshot
Boosting API Recommendation With Implicit Feedback
inferredcites →https://doi.org/10.1109/tse.2021.3128234openalex-snapshot
An Empirical Study on the Usage of Transformer Models for Code Completion
inferredcites →https://doi.org/10.1109/tse.2022.3197063openalex-snapshot
Revisiting, Benchmarking and Exploring API Recommendation: How Far Are We?
inferredcites →https://doi.org/10.1126/science.abq1158openalex-snapshot
Competition-level code generation with AlphaCode
inferredcites →https://doi.org/10.1145/1595696.1595728openalex-snapshot
Learning from examples to improve code completion systems
inferredcites →https://doi.org/10.1145/2594291.2594321openalex-snapshot
Code completion with statistical language models
inferredcites →https://doi.org/10.1145/2744200openalex-snapshot
Intelligent Code Completion with Bayesian Networks
inferredcites →https://doi.org/10.1145/2950290.2950319openalex-snapshot
Parameter-free probabilistic API mining across GitHub
inferredcites →https://doi.org/10.1145/2950290.2950333openalex-snapshot
API code recommendation using statistical learning from fine-grained changes
inferredcites →https://doi.org/10.1145/2950290.2950334openalex-snapshot
Deep API learning
inferredcites →https://doi.org/10.1145/3238147.3238191openalex-snapshot
API method recommendation without worrying about the task-API knowledge gap
inferredcites →https://doi.org/10.1145/3238147.3238216openalex-snapshot
Effective API recommendation without historical software repositories
inferredcites →https://doi.org/10.1145/3292500.3330699openalex-snapshot
Pythia: AI-assisted Code Completion System
inferredcites →https://doi.org/10.1145/3324884.3416591openalex-snapshot
Multi-task learning based pre-trained language model for code completion
inferredcites →https://doi.org/10.1145/3368089.3417058openalex-snapshot
IntelliCode compose: code generation using transformer
inferredcites →https://doi.org/10.1145/3506696openalex-snapshot
XCode : Towards Cross-Language Code Representation with Large-Scale Pre-Training
inferredcites →https://doi.org/10.1145/3510003.3510096openalex-snapshot
SPT-code
inferredcites →https://doi.org/10.1145/3510003.3510159openalex-snapshot
CLEAR
inferredcites →https://doi.org/10.18293/seke2018-193openalex-snapshot
Learning API Suggestion via Single LSTM Network with Deterministic Negative Sampling
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.275openalex-snapshot
APIRecX: Cross-Library API Recommendation via Pre-Trained Language Model
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/v1/2021.naacl-main.211openalex-snapshot
Unified Pre-training for Program Understanding and Generation
inferredcites →https://doi.org/10.18653/v1/2022.acl-long.499openalex-snapshot
UniXcoder: Unified Cross-Modal Pre-training for Code Representation
inferredcites →https://doi.org/10.18653/v1/n19-1423openalex-snapshot
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
inferredcites →https://doi.org/10.24963/ijcai.2022/775openalex-snapshot
Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code
inferredcites →https://doi.org/10.4230/lipics.cp.2025.31openalex-snapshot
HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case
inferredcites →https://doi.org/10.4230/lipics.esem.2026.34openalex-snapshot
A Direction-Aware Study of LLM-Based Code Translation Across Eight Programming Languages
inferredcites →https://doi.org/10.48550/arxiv.1909.09436openalex-snapshot
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
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.

inferred← citeshttps://doi.org/10.1002/smr.70114openalex-snapshot
Graph Link‐Based Prediction for API Usage Recommendation
inferred← citeshttps://doi.org/10.1007/978-981-95-5012-8_5openalex-snapshot
DHGL: A Dual-Channel Heuristic Greedy Framework for End-to-End Service Solution Construction Under LLM Guidance
inferred← citeshttps://doi.org/10.1007/978-981-95-5012-8_7openalex-snapshot
Optimal Subset Oracle-Based Web API Composition Recommendation for Mashup Creation
inferred← citeshttps://doi.org/10.1007/s10515-025-00518-4openalex-snapshot
Knowledge-guided large language models are trustworthy API recommenders
inferred← citeshttps://doi.org/10.1007/s10515-025-00530-8openalex-snapshot
DPEfficR: a data and parameter efficient approach for training neural API recommendation model
inferred← citeshttps://doi.org/10.1007/s13042-025-02965-7openalex-snapshot
Secure ML: a hybrid defense method to prevent poisoning attacks on machine learning systems
inferred← citeshttps://doi.org/10.1016/j.infsof.2024.107656openalex-snapshot
Automated detection of inter-language design smells in multi-language deep learning frameworks
inferred← citeshttps://doi.org/10.1016/j.infsof.2024.107661openalex-snapshot
An input-denoising-based defense against stealthy backdoor attacks in large language models for code
inferred← citeshttps://doi.org/10.1016/j.iot.2025.101630openalex-snapshot
SOLAR: Illuminating LLM performance in API discovery and service ranking for edge AI and IoT
inferred← citeshttps://doi.org/10.1016/j.neunet.2025.108025openalex-snapshot
Backdoor samples detection based on perturbation discrepancy consistency in pre-trained language models
inferred← citeshttps://doi.org/10.1080/01969722.2025.2483718openalex-snapshot
MCL++ : Strengthening Defenses Against Backdoor Poisoning Attacks
inferred← citeshttps://doi.org/10.1109/ase63991.2025.00285openalex-snapshot
Automated Prompt Generation for Code Intelligence: An Empirical study and Experience in WeChat
inferred← citeshttps://doi.org/10.1109/tr.2026.3664018openalex-snapshot
API Recommendation for Novice Programmers: From Clear Expressions to Effective Results
inferred← citeshttps://doi.org/10.1109/tse.2025.3637777openalex-snapshot
AC2Next: A Novel Model That Can Predict the Next Animation API by Fusing the Animation API Context and the UI Animation Task
inferred← citeshttps://doi.org/10.1142/s0218194024500499openalex-snapshot
Code Recommendation for Schema Evolution of Mimic Storage Systems
inferred← citeshttps://doi.org/10.1145/3695988openalex-snapshot
Large Language Models for Software Engineering: A Systematic Literature Review
inferred← citeshttps://doi.org/10.1145/3707457openalex-snapshot
A Catalog of Data Smells for Coding Tasks
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