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 351–375 of 423 papers

TitleStatusHype
Information-theoretic User Interaction: Significant Inputs for Program Synthesis—0
Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving—0
Interpreting Expert Annotation Differences in Animal Behavior—0
IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis—0
Toward Trustworthy Neural Program Synthesis—0
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities—0
Iterative Genetic Improvement: Scaling Stochastic Program Synthesis—0
Iterative Target Augmentation for Effective Conditional Generation—0
Landmarks and Regions: A Robust Approach to Data Extraction—0
Latent Attention For If-Then Program Synthesis—0
Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages—0
Latent Programmer: Discrete Latent Codes for Program Synthesis—0
Learning a Meta-Solver for Syntax-Guided Program Synthesis—0
Learning-Based Automatic Synthesis of Software Code and Configuration—0
Learning Differentially Private Mechanisms—0
Learning Disjunctions of Predicates—0
Learning Language Structures through Grounding—0
Learning large logic programs by going beyond entailment—0
Learning logic programs by finding minimal unsatisfiable subprograms—0
Learning Neurosymbolic Generative Models via Program Synthesis—0
Learning Semantics-aware Search Operators for Genetic Programming—0
Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis—0
Learning to select examples for program synthesis—0
Learning to Solve Abstract Reasoning Problems with Neurosymbolic Program Synthesis and Task Generation—0
Learning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context—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