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
xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and RetrievalCode1
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
Large Language Models for Code: Security Hardening and Adversarial TestingCode1
Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression Problems—0
Exploring Data Augmentation for Code Generation TasksCode0
NeuRI: Diversifying DNN Generation via Inductive Rule InferenceCode1
Execution-based Code Generation using Deep Reinforcement LearningCode1
PADL: Language-Directed Physics-Based Character ControlCode1
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
Parsel: Algorithmic Reasoning with Language Models by Composing DecompositionsCode2
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
Large Language Models Are Human-Level Prompt EngineersCode3
Synthesizing Programs with Continuous Optimization—0
Generating Sequences by Learning to Self-Correct—0
Graphs, Constraints, and Search for the Abstraction and Reasoning CorpusCode1
Data types as a more ergonomic frontend for Grammar-Guided Genetic ProgrammingCode1
Relational program synthesis with numerical reasoningCode1
Neural-Guided Program Synthesis of Information Extraction Rules Using Self-Supervision—0
Toward Trustworthy Neural Program 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