mlsanityData to 14 Oct 2026

Toward Code Generation: A Survey and Lessons from Semantic Parsing

arXiv 2105.03317 ↗First posted 26 Apr 2021cs.SE cs.CL cs.LGcited by 8

Not on the map. Why?

Abstract (arXiv)

With the growth of natural language processing techniques and demand for improved software engineering efficiency, there is an emerging interest in translating intention from human languages to programming languages. In this survey paper, we attempt to provide an overview of the growing body of research in this space. We begin by reviewing natural language semantic parsing techniques and draw parallels with program synthesis efforts. We then consider semantic parsing works from an evolutionary perspective, with specific analyses on neuro-symbolic methods, architecture, and supervision. We then analyze advancements in frameworks for semantic parsing for code generation. In closing, we present what we believe are some of the emerging open challenges in this domain.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 35 of its 100 references that are in the map. First posted on arXiv 26 Apr 2021.

Builds on

35 of 100 references.

References · 35

  1. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  2. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  3. 2017Snorkel: Rapid Training Data Creation with Weak Supervisioncited by 372
  4. 2018code2vec: Learning Distributed Representations of Codecited by 250
  5. 2017Learning to Represent Programs with Graphscited by 220
  6. 2016Incorporating Copying Mechanism in Sequence-to-Sequence Learningcited by 218
  7. 2018code2seq: Generating Sequences from Structured Representations of Codecited by 179
  8. 2019SPoC: Search-based Pseudocode to Codecited by 176
  9. 2016Language to Logical Form with Neural Attentioncited by 158
  10. 2016DeepCoder: Learning to Write Programscited by 149
  11. 2020Task-Oriented Dialogue as Dataflow Synthesiscited by 122
  12. 2018Coarse-to-Fine Decoding for Neural Semantic Parsingcited by 110
23 more
  1. 2018Mapping Language to Code in Programmatic Contextcited by 104
  2. 2017Learning a Neural Semantic Parser from User Feedbackcited by 99
  3. 2018Tree-to-tree Neural Networks for Program Translationcited by 93
  4. 2017Abstract Syntax Networks for Code Generation and Semantic Parsingcited by 92
  5. 2018TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generationcited by 88
  6. 2019Neo: A Learned Query Optimizercited by 80
  7. 2016Latent Predictor Networks for Code Generationcited by 73
  8. 2019Neural Module Networks for Reasoning over Textcited by 66
  9. 2018Neural Code Comprehension: A Learnable Representation of Code Semanticscited by 59
  10. 2019Does BLEU Score Work for Code Migration?cited by 49
  11. 2019Learning to Infer Program Sketchescited by 44
  12. 2020Improving Compositional Generalization in Semantic Parsingcited by 42
  13. 2021In-IDE Code Generation from Natural Language: Promise and Challengescited by 42
  14. 2019Program Synthesis and Semantic Parsing with Learned Code Idiomscited by 34
  15. 2018Confidence Modeling for Neural Semantic Parsingcited by 33
  16. 2018Aroma: Code Recommendation via Structural Code Searchcited by 29
  17. 2020Obtaining Faithful Interpretations from Compositional Neural Networkscited by 23
  18. 2019Learning Programmatic Idioms for Scalable Semantic Parsingcited by 17
  19. 2018The Three Pillars of Machine Programmingcited by 12
  20. 2016Inferring Logical Forms From Denotationscited by 6
  21. 2018GraphIt: A High-Performance DSL for Graph Analyticscited by 5
  22. 2019Learning Fitness Functions for Machine Programmingcited by 5
  23. 2016Open-Vocabulary Semantic Parsing with both Distributional Statistics and Formal Knowledgecited by 3

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 4; 2024: 1.

8 papers.

  1. 2023CodeFusion: A Pre-trained Diffusion Model for Code Generationcited by 31
  2. 2024PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs)cited by 19
  3. 2022Logical Satisfiability of Counterfactuals for Faithful Explanations in NLIcited by 11
  4. 2023GLUECons: A Generic Benchmark for Learning Under Constraintscited by 9
  5. 2022A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspectivecited by 3
  6. 2023Neural Machine Translation for Code Generationcited by 3
  7. 2023Guess & Sketch: Language Model Guided Transpilationcited by 3
  8. 2022Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executablescited by 2