Type-Directed Program Synthesis for RESTful APIs

TL;DR

APIphany is a novel program synthesizer that uses semantic types and efficient techniques to automatically generate RESTful API call sequences, simplifying complex API interactions for developers.

Contribution

It introduces a type inference algorithm, a data wrangling synthesis method, and a simulated execution approach tailored for REST API composition.

Findings

Successfully synthesized solutions for 29 out of 32 tasks

Achieved top-ten results for 23 tasks

Demonstrated effectiveness on real-world APIs

Abstract

With the rise of software-as-a-service and microservice architectures, RESTful APIs are now ubiquitous in mobile and web applications. A service can have tens or hundreds of API methods, making it a challenge for programmers to find the right combination of methods to solve their task. We present APIphany, a component-based synthesizer for programs that compose calls to RESTful APIs. The main innovation behind APIphany is the use of precise semantic types, both to specify user intent and to direct the search. APIphany contributes three novel mechanisms to overcome challenges in adapting component-based synthesis to the REST domain: (1) a type inference algorithm for augmenting REST specifications with semantic types; (2) an efficient synthesis technique for "wrangling" semi-structured data, which is commonly required in working with RESTful APIs; and (3) a new form of simulated execution to avoid executing APIs calls during synthesis. We evaluate APIphany on three real-world APIs and 32 tasks extracted from GitHub repositories and StackOverflow. In our experiments, APIphany found correct solutions to 29 tasks, with 23 of them reported among top ten synthesis results.