University of Illinois Urbana-Champaign

Code analysis and rewriting for data debloating and improved fuzz testing

Niddodi, Chaitra Prasad

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https://hdl.handle.net/2142/121205

Description

Title
Code analysis and rewriting for data debloating and improved fuzz testing
Author(s)
Niddodi, Chaitra Prasad
Issue Date
2023-06-26
Director of Research (if dissertation) or Advisor (if thesis)
  • Mohan, Sibin
  • Marinov, Darko
Doctoral Committee Chair(s)
  • Mohan, Sibin
  • Marinov, Darko
Committee Member(s)
  • Misailovic, Sasa
  • Rilee, Michael Lee
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Code Rewriting, Data debloating, Fuzz Testing
Abstract
Code rewriting has predominantly been used for the purposes of improving security and reducing code bloat in software. This research explores the applicability of code rewriting in the contexts of data-based debloating [IOSPReD] and in enhancing fuzz testing [TOPr]. IOSPReD focuses on reducing the amount of data that is included with an application to the extent of its requirement. To this end, IOSPReD automatically tracks and packages only necessary data chunks along with its application, in a container. In particular, a reduced datastore is generated and the underlying I/O calls in the program are rewritten via I/O specialization to access the reduced data accurately. TOPr focuses on target-oriented pruning to reduce the exploration space during code execution. TOPr eliminates unnecessary code that do not lead to specific target locations. Specifically, the program code is rewritten to redirect control flow only towards paths leading to the target location. In an attempt to improve fuzz testing towards predefined target locations in code, we integrate TOPr with a popularly used directed fuzzer, AFLGo.
Graduation Semester
2023-08
Type of Resource
Thesis
Copyright and License Information
Copyright 2023 Chaitra Prasad Niddodi

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