# Toward Code Generation: A Survey and Lessons from Semantic Parsing

arXiv: https://arxiv.org/abs/2105.03317 · HTML: https://mlsanity.com/paper/2105.03317/

## 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 (most cited first, 30 of 35)

- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [Learning to Represent Programs with Graphs](https://mlsanity.com/paper/1711.00740/) (2017)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018)
- [SPoC: Search-based Pseudocode to Code](https://mlsanity.com/paper/1906.04908/) (2019)
- [Language to Logical Form with Neural Attention](https://mlsanity.com/paper/1601.01280/) (2016)
- [DeepCoder: Learning to Write Programs](https://mlsanity.com/paper/1611.01989/) (2016)
- [Task-Oriented Dialogue as Dataflow Synthesis](https://mlsanity.com/paper/2009.11423/) (2020)
- [Coarse-to-Fine Decoding for Neural Semantic Parsing](https://mlsanity.com/paper/1805.04793/) (2018)
- [Mapping Language to Code in Programmatic Context](https://mlsanity.com/paper/1808.09588/) (2018)
- [Learning a Neural Semantic Parser from User Feedback](https://mlsanity.com/paper/1704.08760/) (2017)
- [Tree-to-tree Neural Networks for Program Translation](https://mlsanity.com/paper/1802.03691/) (2018)
- [Abstract Syntax Networks for Code Generation and Semantic Parsing](https://mlsanity.com/paper/1704.07535/) (2017)
- [TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation](https://mlsanity.com/paper/1810.02720/) (2018)
- [Neo: A Learned Query Optimizer](https://mlsanity.com/paper/1904.03711/) (2019)
- [Latent Predictor Networks for Code Generation](https://mlsanity.com/paper/1603.06744/) (2016)
- [Neural Module Networks for Reasoning over Text](https://mlsanity.com/paper/1912.04971/) (2019)
- [Neural Code Comprehension: A Learnable Representation of Code Semantics](https://mlsanity.com/paper/1806.07336/) (2018)
- [Does BLEU Score Work for Code Migration?](https://mlsanity.com/paper/1906.04903/) (2019)
- [Learning to Infer Program Sketches](https://mlsanity.com/paper/1902.06349/) (2019)
- [Improving Compositional Generalization in Semantic Parsing](https://mlsanity.com/paper/2010.05647/) (2020)
- [In-IDE Code Generation from Natural Language: Promise and Challenges](https://mlsanity.com/paper/2101.11149/) (2021)
- [Program Synthesis and Semantic Parsing with Learned Code Idioms](https://mlsanity.com/paper/1906.10816/) (2019)
- [Confidence Modeling for Neural Semantic Parsing](https://mlsanity.com/paper/1805.04604/) (2018)
- [Aroma: Code Recommendation via Structural Code Search](https://mlsanity.com/paper/1812.01158/) (2018)
- [Obtaining Faithful Interpretations from Compositional Neural Networks](https://mlsanity.com/paper/2005.00724/) (2020)
- [Learning Programmatic Idioms for Scalable Semantic Parsing](https://mlsanity.com/paper/1904.09086/) (2019)

## Built on by (most cited first, 8 of 8)

- [CodeFusion: A Pre-trained Diffusion Model for Code Generation](https://mlsanity.com/paper/2310.17680/) (2023)
- [PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs)](https://mlsanity.com/paper/2409.12699/) (2024)
- [Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI](https://mlsanity.com/paper/2205.12469/) (2022)
- [GLUECons: A Generic Benchmark for Learning Under Constraints](https://mlsanity.com/paper/2302.10914/) (2023)
- [A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective](https://mlsanity.com/paper/2202.04847/) (2022)
- [Neural Machine Translation for Code Generation](https://mlsanity.com/paper/2305.13504/) (2023)
- [Guess & Sketch: Language Model Guided Transpilation](https://mlsanity.com/paper/2309.14396/) (2023)
- [Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables](https://mlsanity.com/paper/2209.01566/) (2022)

## 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.