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 101–125 of 423 papers

TitleStatusHype
ChatGPT for GTFS: Benchmarking LLMs on GTFS Understanding and RetrievalCode0
Neural Program Synthesis from Diverse Demonstration VideosCode0
NAPS: Natural Program Synthesis DatasetCode0
Enhancing Code Generation via Bidirectional Comment-Level Mutual GroundingCode0
Mapping Natural-language Problems to Formal-language Solutions Using Structured Neural RepresentationsCode0
MathDSL: A Domain-Specific Language for Concise Mathematical Solutions Via Program SynthesisCode0
LTL learning on GPUsCode0
Making sense of sensory inputCode0
Memory Augmented Policy Optimization for Program Synthesis and Semantic ParsingCode0
Neural Guided Constraint Logic Programming for Program SynthesisCode0
Learning to Parallelize with OpenMP by Augmented Heterogeneous AST RepresentationCode0
Learning to Reason via Program Generation, Emulation, and SearchCode0
Dynamic Neural Program Embedding for Program RepairCode0
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence GenerationCode0
Learning to Infer Graphics Programs from Hand-Drawn ImagesCode0
BF++: a language for general-purpose program synthesisCode0
Learning to Execute Programs with Instruction Pointer Attention Graph Neural NetworksCode0
Evaluating Sequence-to-Sequence Learning Models for If-Then Program SynthesisCode0
Learning to Infer Program SketchesCode0
Learning MDL logic programs from noisy dataCode0
Differentiable Functional Program InterpretersCode0
A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selectionCode0
Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning EnvironmentsCode0
Learning logic programs by discovering where not to searchCode0
Learning logic programs by discovering higher-order abstractionsCode0
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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