Automatically identifying critical behaviors in programs
Name
599812329-MIT.pdf
Description
Full printable version
Size
3.19 MB
Format
Adobe PDF
Checksum (MD5)
408a67992921b753af50b59b4ef2b76f
Author(s)
Carbin, Michael (Michael James)
Advisor(s)
Martin C. Rinard.
Date Issued
2009
Publisher
Massachusetts Institute of Technology
Abstract
The large size of modern software systems has led to an increase in the complexity of the interaction between a system's code, its input, and its output. I propose the following classifications for the regions of a system's input: * Critical control: data that influences the internal operation and output of the system. * Critical payload: data that heavily contributes to the output of the program but does not substantially influence the internal operation of the program. * Benign control: data that influences the internal operation of the system, but does not contribute to the output of the system. * Benign payload: data that neither contributes to the output nor substantially influences the internal operation of the program. In this thesis, I present Chaos, a system designed to automatically infer these classifications for a program's inputs and code. Chaos monitors the execution trace and dynamic taint trace of an application over a suite of inputs to determine how regions of the programs' code and input influence its behavior and output. This thesis demonstrates the accuracy of Chaos's classifications for a set of imaging applications and their support libraries. These automatically inferred classifications are relevant to a variety of software engineering tasks, including program understanding, maintenance, debugging, testing, and defect correction triage.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 60-63).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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