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 251–275 of 423 papers

TitleStatusHype
Refinement Type Directed Search for Meta-Interpretive-Learning of Higher-Order Logic Programs—0
Reinforcement Learning and Data-Generation for Syntax-Guided Synthesis—0
Report of the Workshop on Program Synthesis for Scientific Computing—0
Representing Partial Programs with Blended Abstract Semantics—0
Research Vision: Multi-Agent Path Planning for Cops And Robbers Via Reactive Synthesis—0
RLSF: Reinforcement Learning via Symbolic Feedback—0
Runtime phylogenetic analysis enables extreme subsampling for test-based problems—0
Sampling for Bayesian Program Learning—0
SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions—0
Satisfiability and Synthesis Modulo Oracles—0
Self-Training Large Language Models for Improved Visual Program Synthesis With Visual Reinforcement—0
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models—0
Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study—0
SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications—0
Shedding Light in Task Decomposition in Program Synthesis: The Driving Force of the Synthesizer Model—0
Solving Linear Algebra by Program Synthesis—0
Solving Linear Algebra by Program Synthesis—0
Solving Novel Program Synthesis Problems with Genetic Programming using Parametric Polymorphism—0
Solving Probability and Statistics Problems by Program Synthesis—0
Solving Probability and Statistics Problems by Program Synthesis—0
Solving Visual Analogies Using Neural Algorithmic Reasoning—0
SpreadsheetCoder: Formula Prediction from Semi-structured Context—0
Statically Contextualizing Large Language Models with Typed Holes—0
Synthesizing Datalog Programs Using Numerical Relaxation—0
Structured Program Synthesis using LLMs: Results and Insights from the IPARC Challenge—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