Modeling Black-Box Components with Probabilistic Synthesis
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Abstract (arXiv)
This paper is concerned with synthesizing programs based on black-box oracles: we are interested in the case where there exists an executable implementation of a component or library, but its internal structure is unknown. We are provided with just an API or function signature, and aim to synthesize a program with equivalent behavior. To attack this problem, we detail Presyn: a program synthesizer designed for flexible interoperation with existing programs and compiler toolchains. Presyn uses high-level imperative control-flow structures and a pair of cooperating predictive models to efficiently narrow the space of potential programs. These models can be trained effectively on small corpora of synthesized examples. We evaluate Presyn against five leading program synthesizers on a collection of 112 synthesis benchmarks collated from previous studies and real-world software libraries. We show that Presyn is able to synthesize a wider range of programs than each of them with less human input. We demonstrate the application of our approach to real-world code and software engineering problems with two case studies: accelerator library porting and detection of duplicated library reimplementations.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Oct 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2; 2024: 2; 2025: 0; 2026: 2.
8 papers.
- 2024LLM4Decompile: Decompiling Binary Code with Large Language Modelscited by 38
- 2023SLaDe: A Portable Small Language Model Decompiler for Optimized Assemblycited by 28
- 2021Learning C to x86 Translation: An Experiment in Neural Compilationcited by 12
- 2024Forklift: An Extensible Neural Liftercited by 2
- 2021Satisfiability and Synthesis Modulo Oraclescited by 1
- 2026From I/O to Code with Discovery Agentcited by 1
- 2023mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesiscited by 0
- 2026Echo: Learning-based Matching Decompilation using Trusted Back Translationcited by 0
- 2026Echo: Learning-based Matching Decompilation using Trusted Back Translation16 Sep 2026
- 2026From I/O to Code with Discovery Agent14 May 2026
- 2024Forklift: An Extensible Neural Lifter1 Apr 2024
- 2024LLM4Decompile: Decompiling Binary Code with Large Language Models8 Mar 2024
- 2023mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesis6 Oct 2023
- 2023SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly21 May 2023
- 2021Learning C to x86 Translation: An Experiment in Neural Compilation17 Aug 2021
- 2021Satisfiability and Synthesis Modulo Oracles28 Jul 2021