# Learning to Synthesize

arXiv: https://arxiv.org/abs/1802.07608 · HTML: https://mlsanity.com/paper/1802.07608/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2018.

## Built on by (most cited first, 5 of 5)

- [A Syntax-Guided Edit Decoder for Neural Program Repair](https://mlsanity.com/paper/2106.08253/) (2021)
- [A Survey on Machine Learning Techniques for Source Code Analysis](https://mlsanity.com/paper/2110.09610/) (2021)
- [DCDetector: An IoT terminal vulnerability mining system based on distributed deep ensemble learning under source code representation](https://mlsanity.com/paper/2211.16235/) (2022)
- [VMCDL: Vulnerability Mining Based on Cascaded Deep Learning Under Source Control Flow](https://mlsanity.com/paper/2303.07128/) (2023)
- [Learning to Synthesize Graphics Programs for Geometric Artworks](https://mlsanity.com/paper/2410.15768/) (2024)

## Abstract (arXiv)

In many scenarios we need to find the most likely program under a local context, where the local context can be an incomplete program, a partial specification, natural language description, etc. We call such problem program estimation. In this paper we propose an abstract framework, learning to synthesis, or L2S in short, to address this problem. L2S combines four tools to achieve this: syntax is used to define the search space and search steps, constraints are used to prune off invalid candidates at each search step, machine-learned models are used to estimate conditional probabilities for the candidates at each search step, and search algorithms are used to find the best possible solution. The main goal of L2S is to lay out the design space to motivate the research on program estimation. We have performed a preliminary evaluation by instantiating this framework for synthesizing conditions of an automated program repair (APR) system. The training data are from the project itself and related JDK packages. Compared to ACS, a state-of-the-art condition synthesis system for program repair, our approach could deal with a larger search space such that we fixed 4 additional bugs outside the search space of ACS, and relies only on the source code of the current projects.