Synthesizing Bijective Lenses

TL;DR

This paper introduces Optician, a type-directed synthesis tool for creating bijective string transformers using regular expressions, improving efficiency and correctness in bidirectional data transformations.

Contribution

We present Optician, a novel synthesis system that efficiently generates well-typed bijective string transformers from regular expressions and examples, with proven correctness.

Findings

Optician significantly reduces synthesis complexity compared to prior methods.

The system successfully synthesizes transformations for diverse real-world data formats.

Empirical evaluation shows high efficiency and correctness across benchmark examples.

Abstract

Bidirectional transformations between different data representations occur frequently in modern software systems. They appear as serializers and deserializers, as database views and view updaters, and more. Manually building bidirectional transformations---by writing two separate functions that are intended to be inverses---is tedious and error prone. A better approach is to use a domain-specific language in which both directions can be written as a single expression. However, these domain-specific languages can be difficult to program in, requiring programmers to manage fiddly details while working in a complex type system. To solve this, we present Optician, a tool for type-directed synthesis of bijective string transformers. The inputs to Optician are two ordinary regular expressions representing two data formats and a few concrete examples for disambiguation. The output is a well-typed program in Boomerang (a bidirectional language based on the theory of lenses). The main technical challenge involves navigating the vast program search space efficiently enough. Unlike most prior work on type-directed synthesis, our system operates in the context of a language with a rich equivalence relation on types (the theory of regular expressions). We synthesize terms of a equivalent language and convert those generated terms into our lens language. We prove the correctness of our synthesis algorithm. We also demonstrate empirically that our new language changes the synthesis problem from one that admits intractable solutions to one that admits highly efficient solutions. We evaluate Optician on a benchmark suite of 39 examples including both microbenchmarks and realistic examples derived from other data management systems including Flash Fill, a tool for synthesizing string transformations in spreadsheets, and Augeas, a tool for bidirectional processing of Linux system configuration files.