{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/55143"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/55143","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Automatically identifying critical behaviors in programs","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_html":"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_has_math":false,"creators":["Carbin, Michael (Michael James)"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Martin C. Rinard."],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009","date_published":"2009","updated_at":"2026-07-22T22:21:24Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/55143","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Martin C. Rinard."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Automatically identifying critical behaviors in programs"]}]}],"canonical_facts":{"dc:contributor.advisor":["Martin C. Rinard."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. 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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."],"dc:description.degree":["S.M."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/55143"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["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."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Automatically identifying critical behaviors in programs"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:21:24Z"}