Program Synthesis
138 papers with code • 3 benchmarks • 5 datasets
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.
Benchmarks
These leaderboards are used to track progress in Program Synthesis
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Libraries
Use these libraries to find Program Synthesis models and implementations
CONCODE
SPoC
SketchGraphs
xCodeEval
Subtasks
Latest papers
CodeScholar: Growing Idiomatic Code Examples
A tool that could generate realistic, idiomatic, and contextual usage examples for one or more APIs would be immensely beneficial to developers.
Automating the Design of Multigrid Methods with Evolutionary Program Synthesis
For this purpose, we develop a novel context-free grammar that enables the automated generation of multigrid methods in a symbolically-manipulable formal language, based on which we can apply the same multigrid-based solver to problems of different sizes without having to adapt its internal structure.
KEN: Kernel Extensions using Natural Language
In particular, the system uses symbolic execution in a novel structure that allows it to combine the results of program synthesis and program comprehension and build on the recent success that LLMs have shown for each of these tasks individually.
Bring Your Own KG: Self-Supervised Program Synthesis for Zero-Shot KGQA
We present BYOKG, a universal question-answering (QA) system that can operate on any knowledge graph (KG), requires no human-annotated training data, and can be ready to use within a day -- attributes that are out-of-scope for current KGQA systems.
Generating Pragmatic Examples to Train Neural Program Synthesizers
saujasv/generating-pragmatic-examples
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Programming-by-example is the task of synthesizing a program that is consistent with a set of user-provided input-output examples.
LILO: Learning Interpretable Libraries by Compressing and Documenting Code
While large language models (LLMs) now excel at code generation, a key aspect of software development is the art of refactoring: consolidating code into libraries of reusable and readable programs.
Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning Environments
Understanding the interactions of agents trained with deep reinforcement learning is crucial for deploying agents in games or the real world.
Amortizing Pragmatic Program Synthesis with Rankings
We prove that for a pragmatic synthesizer that uses a single demonstration, our global ranking method exactly replicates RSA's ranked responses.
Learning MDL logic programs from noisy data
Many inductive logic programming approaches struggle to learn programs from noisy data.
Enhancing Network Management Using Code Generated by Large Language Models
Analyzing network topologies and communication graphs plays a crucial role in contemporary network management.
