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 276–300 of 423 papers

TitleStatusHype
SpreadsheetCoder: Formula Prediction from Semi-structured Context—0
Leveraging Language to Learn Program Abstractions and Search Heuristics—0
Communicating Natural Programs to Humans and MachinesCode1
Learning to Combine Per-Example Solutions for Neural Program SynthesisCode1
Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology ProblemsCode0
Interpreting Expert Annotation Differences in Animal Behavior—0
PSB2: The Second Program Synthesis Benchmark Suite—0
Programming PuzzlesCode1
Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages—0
Program Synthesis as Dependency Quantified Formula Modulo TheoryCode0
Automated Decision-based Adversarial Attacks—0
Inductive Program Synthesis over Noisy Datasets using Abstraction Refinement Based Optimization—0
Toward Code Generation: A Survey and Lessons from Semantic Parsing—0
CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance ComputingCode1
Geometry of Program Synthesis—0
Toward Neural-Network-Guided Program Synthesis and VerificationCode0
Program Synthesis Over Noisy Data with Guarantees—0
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics—0
Program Synthesis Guided Reinforcement Learning for Partially Observed EnvironmentsCode1
Refinement Type Directed Search for Meta-Interpretive-Learning of Higher-Order Logic Programs—0
Report of the Workshop on Program Synthesis for Scientific Computing—0
BF++: a language for general-purpose program synthesisCode0
Analysis of Evolutionary Program Synthesis for Card GamesCode0
Learning Differentially Private Mechanisms—0
Visible and Invisible: Causal Variable Learning and its Application in a Cancer Study—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