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Architecture-Agnostic Dynamic Type Recovery

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

Programmers can use various data types when developing software. However, if the program is compiled to machine code, most of this type information is lost. If analysis of a compiled program is necessary, the lost data types have to be recovered again, to make the code understandable. Existing approaches for the type recovery problem require detailed knowledge about the CPU architecture in question. An architecture-agnostic approach is missing so far. This work focuses on a truly architecture-agnostic type recovery algorithm, implemented in a dynamic analysis system. It can recover data types using minimal knowledge about the CPU architecture, therefore, making it easy to support many different CPU architectures in the analysis system.
OriginalspracheEnglisch
TitelMPLR 2021: Proceedings of the 18th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes
Herausgeber*innenHerbert Kuchen, Singer Jeremy
VerlagACM Digital
Seiten54-60
Seitenumfang7
ISBN (elektronisch)9781450386753
DOIs
PublikationsstatusVeröffentlicht - 29 Sep. 2021

Wissenschaftszweige

  • 102 Informatik
  • 102009 Computersimulation
  • 102011 Formale Sprachen
  • 102013 Human-Computer Interaction
  • 102022 Softwareentwicklung
  • 102024 Usability Research
  • 102029 Praktische Informatik

JKU-Schwerpunkte

  • Digital Transformation

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