# Component-based Synthesis of Table Consolidation and Transformation Tasks from Examples

arXiv: https://arxiv.org/abs/1611.07502 · HTML: https://mlsanity.com/paper/1611.07502/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2016.

## Built on by (most cited first, 10 of 10)

- [Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code?](https://mlsanity.com/paper/2306.01220/) (2023)
- [Multi-modal Program Inference: a Marriage of Pre-trainedLanguage Models and Component-based Synthesis](https://mlsanity.com/paper/2109.02445/) (2021)
- [SEVerA: Verified Synthesis of Self-Evolving Agents](https://mlsanity.com/paper/2603.25111/) (2026)
- [CORNET: Learning Table Formatting Rules By Example](https://mlsanity.com/paper/2208.06032/) (2022)
- [ImageEye: Batch Image Processing Using Program Synthesis](https://mlsanity.com/paper/2304.03253/) (2023)
- [SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions](https://mlsanity.com/paper/2202.10451/) (2022)
- [Inductive Program Synthesis over Noisy Datasets using Abstraction Refinement Based Optimization](https://mlsanity.com/paper/2104.13315/) (2021)
- [Program Synthesis Over Noisy Data with Guarantees](https://mlsanity.com/paper/2103.05030/) (2021)
- [Deterministic Graph-Walking Program Mining](https://mlsanity.com/paper/2208.10290/) (2022)
- [APRIL: API Synthesis with Automatic Prompt Optimization and Reinforcement Learning](https://mlsanity.com/paper/2509.25196/) (2025)

## Abstract (arXiv)

This paper presents an example-driven synthesis technique for automating a large class of data preparation tasks that arise in data science. Given a set of input tables and an out- put table, our approach synthesizes a table transformation program that performs the desired task. Our approach is not restricted to a fixed set of DSL constructs and can synthesize programs from an arbitrary set of components, including higher-order combinators. At a high-level, our approach performs type-directed enumerative search over partial pro- grams but incorporates two key innovations that allow it to scale: First, our technique can utilize any first-order specification of the components and uses SMT-based deduction to reject partial programs. Second, our algorithm uses partial evaluation to increase the power of deduction and drive enumerative search. We have evaluated our synthesis algorithm on dozens of data preparation tasks obtained from on-line forums, and we show that our approach can automatically solve a large class of problems encountered by R users.