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
From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams—0
Program Machine Policy: Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines—0
A Divide-Align-Conquer Strategy for Program Synthesis—0
Adversarial Robustness of Program Synthesis Models—0
Adversarial Synthetic Datasets for Neural Program Synthesis—0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking—0
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics—0
Ain't Nobody Got Time For Coding: Structure-Aware Program Synthesis From Natural Language—0
AlgoPilot: Fully Autonomous Program Synthesis Without Human-Written Programs—0
Amortizing Pragmatic Program Synthesis with Rankings—0
Analogical proportions II—0
Untangling the Effects of Down-Sampling and Selection in Genetic Programming—0
A Neurodiversity-Inspired Solver for the Abstraction \& Reasoning Corpus (ARC) Using Visual Imagery and Program Synthesis—0
An Empirical Comparison of Cost Functions in Inductive Logic Programming—0
A Neurosymbolic Approach to Adaptive Feature Extraction in SLAM—0
A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis—0
Are Transformers All That Karel Needs?—0
A Survey of Methods for Converting Unstructured Data to CSG Models—0
A Survey on Semantic Parsing—0
AutoCoder: Leveraging Transformers for Automatic Code Synthesis—0
Autoformalization with Large Language Models—0
Automated Decision-based Adversarial Attacks—0
Automated Generation of Executable Cross-Language Background Knowledge—0
AutoTSG: Learning and Synthesis for Incident Troubleshooting—0
AutumnSynth: Synthesis of Reactive Programs with Structured Latent State—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