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Title: Type-Directed Program Synthesis for RESTful APIs
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.  more » « less
Award ID(s):
1943623
PAR ID:
10320183
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation
ISSN:
1531-7102
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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