Awesome Program

Description

This repo contains a curated list of papers related to program synthesis, program induction, program execution, program and code repair, and programmatic reinforcement learning. This list is maintained by Shao-Hua Sun. You are more than welcome to contribute by suggesting changes to make the list more comprehensive or correcting errors. Please simply create a pull request or contact me via email. If you find this repo useful for your research, please consider citing it.

Content

• Program Synthesis
• Program Induction
• Program Execution
• Program and Code Repair
• Programmatic Reinforcement Learning

Program Synthesis

The goal of program synthesis is to construct a human-readable program that satisfies task specifications represented as input/output pairs, demonstrations, natural language instructions, etc. Recent works also explore representing images and 3D shapes using programs, allowing applications such as editing, scene understanding, analogy-making, reverse engineering, etc.

• 31 papers

Paper Title	Conference	Year	Link
Latent Attention For If-Then Program Synthesis	NIPS	2016	link
Neural Scene De-rendering	CVPR	2017	link
DeepCoder: Learning to Write Programs	ICLR	2017	link
Neuro-Symbolic Program Synthesis	ICLR	2017	link
RobustFill: Neural Program Learning under Noisy I/O	ICML	2017	link
Differentiable Programs with Neural Libraries	ICML	2017	link
pix2code: Generating Code from a Graphical User Interface Screenshot	arXiv	2017	link
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning	arXiv	2017	link
NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System	ELRA	2018	link
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis	ICLR	2018	link
Towards Synthesizing Complex Programs from Input-Output Examples	ICLR	2018	link
Neural Program Synthesis from Diverse Demonstration Videos	ICML	2018	link
Improving Neural Program Synthesis with Inferred Execution Traces	NeurIPS	2018	link
Automatic Program Synthesis of Long Programs with a Learned Garbage Collector	NeurIPS	2018	link
Neural Program Synthesis with Priority Queue Training	arXiv	2018	link
Recent Advances in Neural Program Synthesis	arXiv	2018	link
Watch, Reason and Code- Learning to Represent Videos Using Program	ACM Multimedia	2019	link
Program-Guided Image Manipulators	ICCV	2019	link
Execution-Guided Neural Program Synthesis	ICLR	2019	link
Learning to Describe Scenes with Programs	ICLR	2019	link
Learning to Infer and Execute 3D Shape Programs	ICLR	2019	link
Synthetic Datasets for Neural Program Synthesis	ICLR	2019	link
Ain’t Nobody Got Time For Coding: Structure-Aware Program Synthesis From Natural Language	arXiv	2019	link
Learning Compositional Rules via Neural Program Synthesis	NeurIPS	2020	link
Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis	NeurIPS	2020	link
PLANS: Neuro-Symbolic Program Learning from Videos	NeurIPS	2020	link
Neural Program Synthesis with a Differentiable Fixer	arXiv	2020	link
Learning to Infer Shape Programs Using Self Training	arXiv	2020	link
SpreadsheetCoder: Formula Prediction from Semi-structured Context	ICML	2021	link
Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages	arXiv	2021	link
Learning to Combine Per-Example Solutions for Neural Program Synthesis	arXiv	2021	link

Program Induction

Unlike program synthesis works that are designed to explicitly synthesize programs, program induction methods aim to implicitly induce the underlying programs to mimic the desired behaviors demonstrated in task specifications (e.g. execution traces) to solve algorithmic tasks such as digit addition and string transformations and achieve better generalization.

• 18 papers

Paper Title	Conference	Year	Link
Neural Turing Machines	arXiv	2014	link
Learning Simple Algorithms from Examples	arXiv	2015	link
Reinforcement Learning Neural Turing Machines-Revised	arXiv	2015	link
Neural GPUs Learn Algorithms	arXiv	2015	link
Neural Random-Access Machines	arXiv	2015	link
Neural Programmer-Interpreters	ICLR	2016	link
Extensions and Limitations of the Neural GPU	arXiv	2016	link
Neural Program Lattices	ICLR	2017	link
Making Neural Programming Architectures Generalize via Recursion	ICLR	2017	link
Neural Program Meta-Induction	NIPS	2017	link
Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction	ICLR	2018	link
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks	ICRA	2018	link
Memory Augmented Policy Optimization for Program Synthesis with Generalization	NeurIPS	2018	link
Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration	CVPR	2019	link
Learning Compositional Neural Programs with Recursive Tree Search and Planning	NeurIPS	2019	link
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer Architecture	arXiv	2019	link
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning	arXiv	2020	link
Strong Generalization and Efficiency in Neural Programs	arXiv	2020	link

Program Execution

Program execution works explore domains such as utilizing programs as instructions to guide reinforcement learning agents and improving program synthesis performance by executing partially generated programs.

• 9 papers

Paper Title	Conference	Year	Link
Programmable Agents	NIPS	2017	link
Write, Execute, Assess Program Synthesis with a REPL	NeurIPS	2019	link
A Composable Specification Language for Reinforcement Learning Tasks	NeurIPS	2019	link
Program Guided Agent	ICLR	2020	link
Neural Execution Engines- Learning to Execute Subroutines	NeurIPS	2020	link
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks	NeurIPS	2020	link
Reinforcement Learning of Implicit and Explicit Control Flow in Instructions	ICML	2021	link
Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages	arXiv	2021	link
Program Synthesis Guided Reinforcement Learning	arXiv	2021	link

Program and Code Repair

Program and code repair methods aim to automatically identify bugs in code and potentially propose solutions to fix them to alleviate the burden of programmers.

• 11 papers

Paper Title	Conference	Year	Link
SemFix: Program Repair via Semantic Analysis	ICSE	2013	link
DynaMoth: Dynamic Code Synthesis for Automatic Program Repair	AST	2016	link
Leveraging syntax-related code for automated program repair	ASE	2017	link
Contract-Based Program Repair without the Contracts	ASE	2017	link
Shaping program repair space with existing patches and similar code	ACM SIGSOFT	2018	link
LSRepair: Live Search of Fix Ingredients for Automated Program Repair	APSEC	2018	link
Automated Program Repair	CACM	2019	link
Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities	SANER	2019	link
FixMiner: Mining Relevant Fix Patterns for Automated Program Repair	ESEJ	2020	link
Graph-based, Self-Supervised Program Repair from Diagnostic Feedback	ICML	2020	link
DLFix: Context-based Code Transformation Learning for Automated Program Repair	ICSE	2020	link

Programmatic Reinforcement Learning

To solve tasks described by MDPs, the goal of programmatic reinforcement learning is to learn programmatic policies that are more structured, verifiable, interpretable, and generalizable. The following papers represent programmatic policies using representations such as decision trees, finite state machines, or programs structured in domain-specific languages.

• 7 papers

Paper Title	Conference	Year	Link
Programmable Reinforcement Learning Agents	NIPS	2000	link
Programmatically Interpretable Reinforcement Learning	ICML	2018	link
Verifiable Reinforcement Learning via Policy Extraction	NeurIPS	2018	link
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis	arXiv	2018	link
Synthesizing Programmatic Policies that Inductively Generalize	ICLR	2019	link
Learning Finite State Representations of Recurrent Policy Networks	ICLR	2019	link
Imitation-Projected Programmatic Reinforcement Learning	NeurIPS	2019	link

Cite this list

@misc{sun2021programpaperlist,
    author = {Sun, Shao-Hua},
    title = {Program paper list: program synthesis, program induction, program execution, program and code repair, and programmatic reinforcement learning},
    year = {2021},
    journal = {GitHub repository},
    url = {https://github.com/shaohua0116/awesome-program},
}