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Program Synthesis

Program synthesis is the process of automatically generating a program or code snippet that satisfies a given specification or set of requirements. This can include generating code from a formal specification, a natural language description, or example inputs and outputs. The primary goal of program synthesis is to minimize human intervention in the coding process, reduce errors, and improve productivity.

Program synthesis often involves the use of advanced algorithms, artificial intelligence, and machine learning techniques to search the space of possible programs that meet the given constraints. This process can be guided by a variety of techniques, such as constraint solving, symbolic execution, and genetic algorithms.

Papers

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Showing 51–100 of 423 papers

TitleStatusHype
Learning to Synthesize Programs as Interpretable and Generalizable PoliciesCode1
Search-Based Regular Expression Inference on a GPUCode1
Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQACode1
Bug In the Code Stack: Can LLMs Find Bugs in Large Python Code StacksCode1
Outline, Then Details: Syntactically Guided Coarse-To-Fine Code GenerationCode1
Procedural Synthesis of Synthesizable MoleculesCode1
Improving Code Generation by Training with Natural Language FeedbackCode1
TF-Coder: Program Synthesis for Tensor ManipulationsCode1
TinyverseGP: Towards a Modular Cross-domain Benchmarking Framework for Genetic ProgrammingCode1
H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus BenchmarkCode1
Improving Molecular Design by Stochastic Iterative Target AugmentationCode1
CLEVER: A Curated Benchmark for Formally Verified Code GenerationCode1
Graph-based, Self-Supervised Program Repair from Diagnostic FeedbackCode1
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree SearchCode1
Goals as Reward-Producing ProgramsCode1
Graphs, Constraints, and Search for the Abstraction and Reasoning CorpusCode1
CodeScholar: Growing Idiomatic Code ExamplesCode1
Execution-based Code Generation using Deep Reinforcement LearningCode1
Explanatory Learning: Beyond Empiricism in Neural NetworksCode1
From Examples to Rules: Neural Guided Rule Synthesis for Information ExtractionCode1
CodeIt: Self-Improving Language Models with Prioritized Hindsight ReplayCode1
CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance ComputingCode1
CodeUpdateArena: Benchmarking Knowledge Editing on API UpdatesCode1
A Reinforcement Learning Environment for Mathematical Reasoning via Program SynthesisCode1
Code Building Genetic ProgrammingCode1
Guiding Program Synthesis by Learning to Generate ExamplesCode1
Communicating Natural Programs to Humans and MachinesCode1
How Efficient is LLM-Generated Code? A Rigorous & High-Standard BenchmarkCode1
GPIoT: Tailoring Small Language Models for IoT Program Synthesis and DevelopmentCode1
Enhancing Network Management Using Code Generated by Large Language ModelsCode1
Emergent Representations of Program Semantics in Language Models Trained on ProgramsCode1
LambdaBeam: Neural Program Search with Higher-Order Functions and LambdasCode1
Constrained Decoding for Fill-in-the-Middle Code Language Models via Efficient Left and Right Quotienting of Context-Sensitive GrammarsCode1
Learning Compositional Rules via Neural Program SynthesisCode1
Large Language Models for Code: Security Hardening and Adversarial TestingCode1
Automatic Program Synthesis of Long Programs with a Learned Garbage CollectorCode1
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design SequencesCode1
LILO: Learning Interpretable Libraries by Compressing and Documenting CodeCode1
Automating the Design of Multigrid Methods with Evolutionary Program SynthesisCode1
MA-RLHF: Reinforcement Learning from Human Feedback with Macro ActionsCode1
AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz TestingCode1
CrossBeam: Learning to Search in Bottom-Up Program SynthesisCode1
Analyzing the Effectiveness of Large Language Models on Text-to-SQL SynthesisCode1
Data types as a more ergonomic frontend for Grammar-Guided Genetic ProgrammingCode1
Incremental Sampling Without Replacement for Sequence ModelsCode1
PADL: Language-Directed Physics-Based Character ControlCode1
Programming PuzzlesCode1
Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming—0
Chemical classification program synthesis using generative artificial intelligence—0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DrRepairSuccess rate @budget 10038.5—Unverified
2Multiclass localizerSuccess rate @budget 10034.2—Unverified
#ModelMetricClaimedVerifiedStatus
1DrRepairSuccess rate @budget 10057—Unverified
2Multiclass localizerSuccess rate @budget 10053.7—Unverified
#ModelMetricClaimedVerifiedStatus
1CodeTrans-MT-TF-SmallAccuracy90.31—Unverified