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The Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks
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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/s10462-018-09679-zopenalex-snapshot
Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey
inferredcites →https://doi.org/10.1007/s10664-021-09968-2openalex-snapshot
Embedding API dependency graph for neural code generation
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.infsof.2020.106277openalex-snapshot
Mining API usage scenarios from stack overflow
inferredcites →https://doi.org/10.1080/00401706.1989.10488618openalex-snapshot
Statistical Power Analysis for the Behavioral Sciences
inferredcites →https://doi.org/10.1108/lht-02-2022-0103openalex-snapshot
A study on application programming interface recommendation: state-of-the-art techniques, challenges and future directions
inferredcites →https://doi.org/10.1109/ase.2015.36openalex-snapshot
Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation
inferredcites →https://doi.org/10.1109/icse-seip.2019.00042openalex-snapshot
Software Engineering for Machine Learning: A Case Study
inferredcites →https://doi.org/10.1109/icse43902.2021.00041openalex-snapshot
Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks
inferredcites →https://doi.org/10.1109/icse43902.2021.00138openalex-snapshot
Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We?
inferredcites →https://doi.org/10.1109/sai.2014.6918213openalex-snapshot
A survey of feature selection and feature extraction techniques in machine learning
inferredcites →https://doi.org/10.1109/tkde.2005.50openalex-snapshot
Using AUC and accuracy in evaluating learning algorithms
inferredcites →https://doi.org/10.1109/tse.2019.2962027openalex-snapshot
Machine Learning Testing: Survey, Landscapes and Horizons
inferredcites →https://doi.org/10.1109/tse.2020.3018481openalex-snapshot
Deep Learning Based Program Generation From Requirements Text: Are We There Yet?
inferredcites →https://doi.org/10.1109/tse.2021.3074309openalex-snapshot
Holistic Combination of Structural and Textual Code Information for Context Based API Recommendation
inferredcites →https://doi.org/10.1109/tse.2022.3197063openalex-snapshot
Revisiting, Benchmarking and Exploring API Recommendation: How Far Are We?
inferredcites →https://doi.org/10.1145/2393596.2393606openalex-snapshot
Searching connected API subgraph via text phrases
inferredcites →https://doi.org/10.1145/3196398.3196408openalex-snapshot
Learning to mine aligned code and natural language pairs from stack overflow
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/10.1145/3276954openalex-snapshot
Proceedings of the 2018 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software
inferredcites →https://doi.org/10.1145/3368089.3409731openalex-snapshot
API method recommendation via explicit matching of functionality verb phrases
inferredcites →https://doi.org/10.1145/3404835.3462840openalex-snapshot
Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations
inferredcites →https://doi.org/10.1145/3468264.3468611openalex-snapshot
Empirical study of transformers for source code
inferredcites →https://doi.org/10.1145/3540250.3549124openalex-snapshot
API recommendation for machine learning libraries: how far are we?
inferredcites →https://doi.org/10.1609/aaai.v34i05.6430openalex-snapshot
TreeGen: A Tree-Based Transformer Architecture for Code Generation
inferredcites →https://doi.org/10.1613/jair.5477openalex-snapshot
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
inferredcites →https://doi.org/10.18653/v1/2021.acl-longopenalex-snapshot
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
inferredcites →https://doi.org/10.18653/v1/2021.acl-shortopenalex-snapshot
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
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/v1/2023.findings-acl.143openalex-snapshot
AVATAR: A Parallel Corpus for Java-Python Program Translation
inferredcites →https://doi.org/10.18653/v1/d18-1openalex-snapshot
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
inferredcites →https://doi.org/10.18653/v1/d18-2openalex-snapshot
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
inferredcites →https://doi.org/10.18653/v1/d19-1546openalex-snapshot
JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation
inferredcites →https://doi.org/10.18653/v1/p16-1004openalex-snapshot
Language to Logical Form with Neural Attention
inferredcites →https://doi.org/10.18653/v1/p16-1057openalex-snapshot
Latent Predictor Networks for Code Generation
inferredcites →https://doi.org/10.18653/v1/p17-1041openalex-snapshot
A Syntactic Neural Model for General-Purpose Code Generation
inferredcites →https://doi.org/10.18653/v1/p17-1105openalex-snapshot
Abstract Syntax Networks for Code Generation and Semantic Parsing
inferredcites →https://doi.org/10.18653/v1/p18-1068openalex-snapshot
Coarse-to-Fine Decoding for Neural Semantic Parsing
inferredcites →https://doi.org/10.18653/v1/p19-1openalex-snapshot
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
inferredcites →https://doi.org/10.2307/3001968openalex-snapshot
Individual Comparisons by Ranking Methods
inferredcites →https://doi.org/10.24963/ijcai.2022/329openalex-snapshot
CERT: Continual Pre-training on Sketches for Library-oriented Code Generation
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.giscience.2023.43openalex-snapshot
Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics
inferredcites →https://doi.org/10.48550/arxiv.1602.02410openalex-snapshot
Exploring the Limits of Language Modeling
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.2009.08366openalex-snapshot
GraphCodeBERT: Pre-training Code Representations with Data Flow
inferredcites →https://doi.org/10.48550/arxiv.2009.10297openalex-snapshot
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
inferredcites →https://doi.org/10.48550/arxiv.2102.04664openalex-snapshot
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s10515-025-00549-xopenalex-snapshot
Assessing the effectiveness of recent closed-source large language models in fault localization and automated program repair
inferred← citeshttps://doi.org/10.1016/j.cie.2026.112079openalex-snapshot
Industrial mechanism-aware multi-agent collaboration of large language models for job shop scheduling optimization
inferred← citeshttps://doi.org/10.1016/j.inffus.2025.103122openalex-snapshot
From failure to fusion: A survey on learning from bad machine learning models
inferred← citeshttps://doi.org/10.1109/iccsse67426.2025.11552573openalex-snapshot
Automatic generation method of large model code based on associated AST revision
inferred← citeshttps://doi.org/10.1109/msr66628.2025.00082openalex-snapshot
Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
inferred← citeshttps://doi.org/10.1109/tse.2025.3644183openalex-snapshot
Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality
inferred← citeshttps://doi.org/10.1145/3680469openalex-snapshot
Let’s Discover More API Relations: A Large Language Model-Based AI Chain for Unsupervised API Relation Inference
inferred← citeshttps://doi.org/10.1145/3747347openalex-snapshot
Understanding Open Source Contributor Profiles in Popular Machine Learning Libraries
inferred← citeshttps://doi.org/10.1145/3773084openalex-snapshot
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey
inferred← citeshttps://doi.org/10.1145/3816024openalex-snapshot
How Do Large Language Models Perform in Deep Learning Code Generation? An Empirical Study
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