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 176–200 of 423 papers

TitleStatusHype
Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning EnvironmentsCode0
Synthesize, Execute and Debug: Learning to Repair for Neural Program SynthesisCode0
Analysis of Evolutionary Program Synthesis for Card GamesCode0
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis—0
Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming—0
Execution-Guided Neural Program Synthesis—0
Example-based Synthesis of Static Analysis Rules—0
Chemical classification program synthesis using generative artificial intelligence—0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking—0
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation—0
Can LLMs plan paths with extra hints from solvers?—0
Can Large Language Models Understand Symbolic Graphics Programs?—0
Procedural Adherence and Interpretability Through Neuro-Symbolic Generative Agents—0
EgoCoder: Intelligent Program Synthesis with Hierarchical Sequential Neural Network Model—0
Can humans teach machines to code?—0
Efficient Pragmatic Program Synthesis with Informative Specifications—0
Effective Adaptive Mutation Rates for Program Synthesis—0
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration—0
A Neurosymbolic Approach to Adaptive Feature Extraction in SLAM—0
Adversarial Synthetic Datasets for Neural Program Synthesis—0
EcoSearch: A Constant-Delay Best-First Search Algorithm for Program Synthesis—0
Dynamic Neural Program Embeddings for Program Repair—0
Dreaming with ARC—0
Bayesian Program Learning by Decompiling Amortized Knowledge—0
Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression Problems—0
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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