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Automatic Inference of System Software Transformation Rules from Examples

Abstract : The Linux kernel is present today in all kinds of computing environments, from smartphones to supercomputers, including both the latest hardware and "ancient" systems. This multiplicity of environments has come at the expense of a large code size, of approximately ten million lines of code, dedicated to device drivers. However, to add new functionalities, or for performance or security reasons, some internal Application Programming Interfaces (APIs) can be redesigned, triggering the need for changes of potentially thousands of drivers using them. This thesis proposes a novel approach, Spinfer, that can automatically perform these API usage updates. This new approach, based on pattern assembly constrained by control-flow relationships, can learn transformation rules from even imperfect examples. Learned rules are suitable for the challenges found in Linux kernel API usage updates.
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Contributor : Lucas Serrano Connect in order to contact the contributor
Submitted on : Tuesday, January 26, 2021 - 11:50:30 AM
Last modification on : Thursday, June 9, 2022 - 3:41:03 AM
Long-term archiving on: : Tuesday, April 27, 2021 - 6:12:34 PM


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  • HAL Id : tel-03120648, version 1


Lucas Serrano. Automatic Inference of System Software Transformation Rules from Examples. Software Engineering [cs.SE]. Sorbonne Université, 2020. English. ⟨tel-03120648⟩



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