DSI: Automated detection of dynamic data structures in C programs and binary code

Professorship/Faculty: Software Technologies  
Author(s): Rupprecht, Thomas; Boockmann, Jan  ; White, David H.; Lüttgen, Gerald  
By: ... ; White, David; ...
Editors: Amme, Wolfram; Heinze, Thomas
Title of the compilation: Programmiersprachen und Grundlagen der Programmierung : 19. Kolloquium, KPS 2017 ; Weimar, 25.–27. September 2017 ; Tagungsband
Publisher Information: Weimar : Friedrich-Schiller-Universität Jena
Year of publication: 2017
Pages: 134-147
Series ; Volume: Jenaer Schriften zur Mathematik und Informatik ; Math/Inf/ 02 /2017
Language(s): English
URL: http://www.kps2017.uni-jena.de/proceedings/kps2...
Program comprehension is an important task for software engineers who maintain legacy code, as well as for reverse engineers who analyse binary executables such as malware. Detecting dynamic, i.e., pointer-based data structures is a particular challenge due to the complex usage of pointers found in real world software.

This paper presents the key results of the DFG-funded project "Learning Data Structure Behaviour from Executions of Pointer Programs" (DSI), in which dynamic analysis techniques have been developed to identify dynamic data structures in C programs and x86 binary code. DSI's analysis utilizes a novel memory abstraction that allows for a compact description of pointer-based data structures such as linked lists and binary trees, and their interconnections such as parent-child nesting. On top of this abstraction, an evidence-collecting approach calculates a natural language description of the observed data structure with the help of a systematic taxonomy. The inferred data structure information is not only helpful for program comprehension but also for other use cases including software verification and software visualization.
Document Type: Conferenceobject
URI: https://fis.uni-bamberg.de/handle/uniba/44806
Release Date: 20. November 2018