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 176–200 of 423 papers

TitleStatusHype
Amortizing Pragmatic Program Synthesis with Rankings—0
Guiding Enumerative Program Synthesis with Large Language Models—0
Improving Neural Program Synthesis with Inferred Execution Traces—0
GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts—0
Complex QA and language models hybrid architectures, Survey—0
Inductive Program Synthesis over Noisy Datasets using Abstraction Refinement Based Optimization—0
Inference Over Programs That Make Predictions—0
From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams—0
Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving—0
Learning large logic programs by going beyond entailment—0
IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis—0
Comparing and Combining Lexicase Selection and Novelty Search—0
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities—0
GPU accelerated program synthesis: Enumerate semantics, not syntax!—0
Autoformalization with Large Language Models—0
Learning-Based Automatic Synthesis of Software Code and Configuration—0
Goal-directed Generation of Discrete Structures with Conditional Generative Models—0
Glass-Box Program Synthesis: A Machine Learning Approach—0
AutumnSynth: Synthesis of Reactive Programs with Structured Latent State—0
BANSAI: Towards Bridging the AI Adoption Gap in Industrial Robotics with Neurosymbolic Programming—0
Combining LLM Code Generation with Formal Specifications and Reactive Program Synthesis—0
Landmarks and Regions: A Robust Approach to Data Extraction—0
Geometry of Program Synthesis—0
Adaptive Language-Guided Abstraction from Contrastive Explanations—0
ADAPTIVE GENERATION OF PROGRAMMING PUZZLES—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