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 201–250 of 423 papers

TitleStatusHype
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generatorsCode0
Reinforcement Learning and Data-Generation for Syntax-Guided Synthesis—0
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
Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion—0
Coarse-Tuning Models of Code with Reinforcement Learning Feedback—0
Learning-Based Automatic Synthesis of Software Code and Configuration—0
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive CritiquingCode0
Probabilistic Lexicase SelectionCode0
Learning to Parallelize with OpenMP by Augmented Heterogeneous AST RepresentationCode0
From Words to Code: Harnessing Data for Program Synthesis from Natural Language—0
A Survey of Methods for Converting Unstructured Data to CSG Models—0
(Vector) Space is Not the Final Frontier: Product Search as Program Synthesis—0
Fully Autonomous Programming with Large Language Models—0
Untangling the Effects of Down-Sampling and Selection in Genetic Programming—0
ImageEye: Batch Image Processing Using Program Synthesis—0
HOTGP -- Higher-Order Typed Genetic Programming—0
Improved Tree Search for Automatic Program Synthesis—0
Hierarchical Neural Program Synthesis—0
A Neurodiversity-Inspired Solver for the Abstraction \& Reasoning Corpus (ARC) Using Visual Imagery and Program Synthesis—0
Complex QA and language models hybrid architectures, Survey—0
Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression Problems—0
Exploring Data Augmentation for Code Generation TasksCode0
MTGP: Combining Metamorphic Testing and Genetic Programming—0
A Divide-Align-Conquer Strategy for Program Synthesis—0
Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving—0
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities—0
Genetic Algorithm for Program Synthesis—0
Programming by Example and Text-to-Code Translation for Conversational Code Generation—0
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation—0
Synthesizing Programs with Continuous Optimization—0
Generating Sequences by Learning to Self-Correct—0
Neural-Guided Program Synthesis of Information Extraction Rules Using Self-Supervision—0
Toward Trustworthy Neural Program Synthesis—0
Knowledge-Driven Program Synthesis via Adaptive Replacement Mutation and Auto-constructed Subprogram ArchivesCode0
Limits of an AI program for solving college math problems—0
CORNET: Learning Table Formatting Rules By Example—0
PanGu-Coder: Program Synthesis with Function-Level Language ModelingCode0
GitHub Copilot AI pair programmer: Asset or Liability?Code0
From Perception to Programs: Regularize, Overparameterize, and Amortize—0
From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams—0
Functional Code Building Genetic Programming—0
Learning logic programs by combining programsCode0
The Environmental Discontinuity Hypothesis for Down-Sampled Lexicase SelectionCode0
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis—0
Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis—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