SOTAVerified

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 26–50 of 423 papers

TitleStatusHype
A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human LevelCode1
PoE-World: Compositional World Modeling with Products of Programmatic ExpertsCode1
ANPL: Towards Natural Programming with Interactive DecompositionCode1
How Efficient is LLM-Generated Code? A Rigorous & High-Standard BenchmarkCode1
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design SequencesCode1
Guiding Program Synthesis by Learning to Generate ExamplesCode1
Emergent Representations of Program Semantics in Language Models Trained on ProgramsCode1
Enhancing Network Management Using Code Generated by Large Language ModelsCode1
Execution-based Code Generation using Deep Reinforcement LearningCode1
CrossBeam: Learning to Search in Bottom-Up Program SynthesisCode1
AutoIOT: LLM-Driven Automated Natural Language Programming for AIoT ApplicationsCode1
Constrained Decoding for Fill-in-the-Middle Code Language Models via Efficient Left and Right Quotienting of Context-Sensitive GrammarsCode1
Data types as a more ergonomic frontend for Grammar-Guided Genetic ProgrammingCode1
Explanatory Learning: Beyond Empiricism in Neural NetworksCode1
Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQACode1
Large Language Models for Code: Security Hardening and Adversarial TestingCode1
Automatic Program Synthesis of Long Programs with a Learned Garbage CollectorCode1
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learningCode1
Automating the Design of Multigrid Methods with Evolutionary Program SynthesisCode1
AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz TestingCode1
Analyzing the Effectiveness of Large Language Models on Text-to-SQL SynthesisCode1
CodeUpdateArena: Benchmarking Knowledge Editing on API UpdatesCode1
CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance ComputingCode1
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree SearchCode1
CodeScholar: Growing Idiomatic Code ExamplesCode1
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All datasetsSPoC TestPSPoC TestWAlgoLisp

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