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 301–325 of 423 papers

TitleStatusHype
Demonstration of CORNET: A System For Learning Spreadsheet Formatting Rules By Example—0
DeQompile: quantum circuit decompilation using genetic programming for explainable quantum architecture search—0
Design2GarmentCode: Turning Design Concepts to Tangible Garments Through Program Synthesis—0
Differentiable Synthesis of Program Architectures—0
Diffusion On Syntax Trees For Program Synthesis—0
Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression Problems—0
Bayesian Program Learning by Decompiling Amortized Knowledge—0
Dreaming with ARC—0
Dynamic Neural Program Embeddings for Program Repair—0
EcoSearch: A Constant-Delay Best-First Search Algorithm for Program Synthesis—0
Effective Adaptive Mutation Rates for Program Synthesis—0
Efficient Pragmatic Program Synthesis with Informative Specifications—0
EgoCoder: Intelligent Program Synthesis with Hierarchical Sequential Neural Network Model—0
Procedural Adherence and Interpretability Through Neuro-Symbolic Generative Agents—0
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation—0
Example-based Synthesis of Static Analysis Rules—0
Execution-Guided Neural Program Synthesis—0
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis—0
FCR: Flow Chart Recognition Network for Program Synthesis—0
Fewer Truncations Improve Language Modeling—0
Few-Shot Novel Concept Learning for Semantic Parsing—0
Finding structure in logographic writing with library learning—0
Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets—0
From Code to Play: Benchmarking Program Search for Games Using Large Language Models—0
From Perception to Programs: Regularize, Overparameterize, and Amortize—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