2026-09-19T18:46:16Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/551432022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Martin C. Rinard.
Carbin, Michael (Michael James)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-05-25T20:53:18Z
2010-05-25T20:53:18Z
2009
2009
http://hdl.handle.net/1721.1/55143
599812329
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).
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.
by Michael Carbin.
S.M.
63 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Automatically identifying critical behaviors in programs
Thesis
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<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.</Abstract>
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