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Xiaozhu Meng
Researcher at Rice University
Publications - 29
Citations - 337
Xiaozhu Meng is an academic researcher from Rice University. The author has contributed to research in topics: Instrumentation (computer programming) & Computer science. The author has an hindex of 7, co-authored 26 publications receiving 215 citations. Previous affiliations of Xiaozhu Meng include University of Wisconsin-Madison & Huazhong University of Science and Technology.
Papers
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Proceedings ArticleDOI
Binary code is not easy
Xiaozhu Meng,Barton P. Miller +1 more
TL;DR: New code parsing algorithms in the open source Dyninst tool kit are presented, including a new model for describing jump tables that improves the ability to precisely determine the control flow targets, a new interprocedural analysis to determine when a function is non-returning, and techniques for handling tail calls.
Book ChapterDOI
Identifying Multiple Authors in a Binary Program
TL;DR: New code features that capture programming style at the basic block level, an approach for identifying external template library code, and a new approach to capture correlations between the authors of basic blocks in a binary are presented.
Journal ArticleDOI
A Novel Task-Duplication Based Clustering Algorithm for Heterogeneous Computing Environments
TL;DR: A novel Task Duplication based Clustering Algorithm (TDCA) is proposed to improve the schedule performance by utilizing duplication task more thoroughly and improves parameter calculation, task duplication, and task merging.
Proceedings ArticleDOI
Mining Software Repositories for Accurate Authorship
TL;DR: Two new line-level authorship models are presented to overcome the limitation of current tools that assume that the last developer to change a line of code is its author regardless of all earlier changes.
Proceedings ArticleDOI
Fine-grained binary code authorship identification
TL;DR: A new finer-grained technique is presented that can discriminate the author of a basic block with 52% accuracy among 282 authors, as opposed to 0.4% accuracy by random guess, and provides a practical solution for identifying multiple authors in software.