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 126–150 of 423 papers

TitleStatusHype
LLM for SoC Security: A Paradigm Shift—0
The Program Testing Ability of Large Language Models for Code—0
Logic-Q: Improving Deep Reinforcement Learning-based Quantitative Trading via Program Sketch-based Tuning—0
Program Synthesis with Best-First Bottom-Up Search—0
mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesis—0
B-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis—0
Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning EnvironmentsCode0
Amortizing Pragmatic Program Synthesis with RankingsCode0
Learning MDL logic programs from noisy dataCode0
Learning logic programs by discovering higher-order abstractionsCode0
Correct and Optimal: the Regular Expression Inference Challenge—0
Demonstration of CORNET: A System For Learning Spreadsheet Formatting Rules By Example—0
Enhancing Network Management Using Code Generated by Large Language ModelsCode1
ChatGPT for GTFS: Benchmarking LLMs on GTFS Understanding and RetrievalCode0
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis—0
A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis—0
Reinforcement Learning and Data-Generation for Syntax-Guided Synthesis—0
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generatorsCode0
RLTF: Reinforcement Learning from Unit Test FeedbackCode1
Multi-Intent Detection in User Provided Annotations for Programming by Examples Systems—0
Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets—0
Bayesian Program Learning by Decompiling Amortized Knowledge—0
Solving Novel Program Synthesis Problems with Genetic Programming using Parametric Polymorphism—0
Natural Language Commanding via Program Synthesis—0
Knowledge-Driven Robot Program Synthesis from Human VR DemonstrationsCode0
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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