StateFormer: fine-grained type recovery from binaries using generative state modeling-Reference-Cited by-同舟云学术

StateFormer: fine-grained type recovery from binaries using generative state modeling

Published:2021-08-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
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Author:

Pei Kexin¹,Guan Jonas²,Broughton Matthew¹,Chen Zhongtian¹,Yao Songchen¹,Williams-King David¹,Ummadisetty Vikas³,Yang Junfeng¹,Ray Baishakhi¹,Jana Suman¹

Affiliation:

1. Columbia University, USA

2. University of Toronto, Canada

3. Dublin High School, Ireland

Funder

ONR

DiDi Faculty research award

J.P. Morgan Faculty research award

Google Faculty Fellowship

NSF (National Science Foundation)

Amazon Web Services grant

Capital One research grant

ARL Young Investigator (YIP) award

Google Cloud grant

NSF CAREER award

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3468264.3468607

Reference104 articles.

1. National Security Agency. 2019. Ghidra Disassembler. https://ghidra-sre.org/. National Security Agency. 2019. Ghidra Disassembler. https://ghidra-sre.org/.

2. Suggesting accurate method and class names

3. Miltiadis Allamanis Earl T Barr Premkumar Devanbu and Charles Sutton. 2018. A survey of machine learning for big code and naturalness. Comput. Surveys ( 2018 ). Miltiadis Allamanis Earl T Barr Premkumar Devanbu and Charles Sutton. 2018. A survey of machine learning for big code and naturalness. Comput. Surveys ( 2018 ).

4. BinRec

5. Jong-hoon An Avik Chaudhuri Jefrey S Foster and Michael Hicks. 2011. Dynamic inference of static types for Ruby. ACM SIGPLAN Notices ( 2011 ). Jong-hoon An Avik Chaudhuri Jefrey S Foster and Michael Hicks. 2011. Dynamic inference of static types for Ruby. ACM SIGPLAN Notices ( 2011 ).

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