# Synthesizing Abstract Transformers

arXiv: https://arxiv.org/abs/2105.00493 · HTML: https://mlsanity.com/paper/2105.00493/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2021.

## Built on by (most cited first, 1 of 1)

- [SAIL: Sound Abstract Interpreters with LLMs](https://mlsanity.com/paper/2511.13663/) (2025)

## Abstract (arXiv)

This paper addresses the problem of creating abstract transformers automatically. The method we present automates the construction of static analyzers in a fashion similar to the way textityacc automates the construction of parsers. Our method treats the problem as a program-synthesis problem. The user provides specifications of (i) the concrete semantics of a given operation textitop, (ii) the abstract domain A to be used by the analyzer, and (iii) the semantics of a domain-specific language L in which the abstract transformer is to be expressed. As output, our method creates an abstract transformer for textitop in abstract domain A, expressed in L (an "L-transformer for textitop over A"). Moreover, the abstract transformer obtained is a most-precise L-transformer for textitop over A; that is, there is no other L-transformer for textitop over A that is strictly more precise. We implemented our method in a tool called AMURTH. We used AMURTH to create sets of replacement abstract transformers for those used in two existing analyzers, and obtained essentially identical performance. However, when we compared the existing transformers with the transformers obtained using AMURTH, we discovered that four of the existing transformers were unsound, which demonstrates the risk of using manually created transformers.