Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2020

Abstract

In a large software system such as the Linux kernel, there is a continual need for large-scale changes across many source files, triggered by new needs or refined design decisions. In this paper, we propose to ease such changes by suggesting transformation rules to developers, inferred automatically from a collection of examples. Our approach can help automate large-scale changes as well as help understand existing large-scale changes, by highlighting the various cases that the developer who performed the changes has taken into account. We have implemented our approach as a tool, Spinfer. We evaluate Spinfer on a range of challenging large-scale changes from the Linux kernel and obtain rules that achieve 86% precision and 69% recall on average.

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the USENIX Annual Technical Conference (USENIX ATC 2020): July 15-17, 2020

First Page

1

Last Page

14

ISBN

9781939133144

Publisher

USENIX Association

City or Country

Boston

Copyright Owner and License

Authors

Additional URL

https://www.usenix.org/conference/atc20/presentation/serrano

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