# Learning to Represent Programs with Property Signatures

arXiv: https://arxiv.org/abs/2002.09030 · HTML: https://mlsanity.com/paper/2002.09030/

## Key facts

- Cited by 16 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 13 Feb 2020.

## Built on by (most cited first, 16 of 16)

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- [CrossBeam: Learning to Search in Bottom-Up Program Synthesis](https://mlsanity.com/paper/2203.10452/) (2022)
- [ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis](https://mlsanity.com/paper/2307.13883/) (2023)
- [Neural Program Generation Modulo Static Analysis](https://mlsanity.com/paper/2111.01633/) (2021)
- [Learning to Combine Per-Example Solutions for Neural Program Synthesis](https://mlsanity.com/paper/2106.07175/) (2021)
- [Program Synthesis with Best-First Bottom-Up Search](https://mlsanity.com/paper/2310.04327/) (2023)
- [Compositional Generalization and Decomposition in Neural Program Synthesis](https://mlsanity.com/paper/2204.03758/) (2022)
- [LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas](https://mlsanity.com/paper/2306.02049/) (2023)
- [Learning to Improve Code Efficiency](https://mlsanity.com/paper/2208.05297/) (2022)
- [Program Synthesis via Test-Time Transduction](https://mlsanity.com/paper/2509.17393/) (2025)
- [A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspective](https://mlsanity.com/paper/2202.04847/) (2022)
- [Neural Program Synthesis with Query](https://mlsanity.com/paper/2205.07857/) (2022)
- [Weakly Supervised Semantic Parsing with Execution-based Spurious Program Filtering](https://mlsanity.com/paper/2311.01161/) (2023)

## Abstract (arXiv)

We introduce the notion of property signatures, a representation for programs and program specifications meant for consumption by machine learning algorithms. Given a function with input type τin and output type τout, a property is a function of type: (τin, τout) rightarrow textttBool that (informally) describes some simple property of the function under consideration. For instance, if τin and τout are both lists of the same type, one property might ask `is the input list the same length as the output list?'. If we have a list of such properties, we can evaluate them all for our function to get a list of outputs that we will call the property signature. Crucially, we can `guess' the property signature for a function given only a set of input/output pairs meant to specify that function. We discuss several potential applications of property signatures and show experimentally that they can be used to improve over a baseline synthesizer so that it emits twice as many programs in less than one-tenth of the time.