Active learning for inference and regeneration of computer programs that store and retrieve data

As modern computation platforms become increasingly complex, their programming interfaces are increasingly difficult to use. This complexity is especially inappropriate given the relatively simple core functionality that many of the computations implement. We present a new approach for obtaining sof...

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Bibliographic Details
Main Authors: Rinard, Martin C, Shen, Jiasi, Mangalick, Varun
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:English
Published: ACM Press 2020
Online Access:https://hdl.handle.net/1721.1/125749
Description
Summary:As modern computation platforms become increasingly complex, their programming interfaces are increasingly difficult to use. This complexity is especially inappropriate given the relatively simple core functionality that many of the computations implement. We present a new approach for obtaining software that executes on modern computing platforms with complex programming interfaces. Our approach starts with a simple seed program, written in the language of the developer's choice, that implements the desired core functionality. It then systematically generates inputs and observes the resulting outputs to learn the core functionality. It finally automatically regenerates new code that implements the learned core functionality on the target computing platform. This regenerated code contains boilerplate code for the complex programming interfaces that the target computing platform presents. By providing a productive new mechanism for capturing and encapsulating knowledge about how to use modern complex interfaces, this new approach promises to greatly reduce the developer effort required to obtain secure, robust software that executes on modern computing platforms.