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Michael D. Ernst. Guo, Philip Jia Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2007-03-12T17:51:53Z 2007-03-12T17:51:53Z 2006 2006 http://hdl.handle.net/1721.1/36767 78924529 Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006. Includes bibliographical references (p. 107-112). This thesis addresses the difficult task of constructing robust and scalable dynamic program analysis tools for programs written in memory-unsafe languages such as C and C++, especially those that are interested in observing the contents of data structures at run time. In this thesis, I first introduce my novel mixed-level approach to dynamic analysis, which combines the advantages of both source- and binary-based approaches. Second, I present a tool framework that embodies the mixed-level approach. This framework provides memory safety guarantees, allows tools built upon it to access rich source- and binary-level information simultaneously at run time, and enables tools to scale to large, real-world C and C++ programs on the order of millions of lines of code. Third, I present two dynamic analysis tools built upon my framework - one for performing value profiling and the other for performing dynamic inference of abstract types - and describe how they far surpass previous analyses in terms of scalability, robustness, and applicability. Lastly, I present several case studies demonstrating how these tools aid both humans and automated tools in several program analysis tasks: improving human understanding of unfamiliar code, invariant detection, and data structure repair. by Philip Jia Guo. M.Eng. 112 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. A scalable mixed-level approach to dynamic analysis of C and C++ programs Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="3022c7b3-840c-48c1-8777-fbe3de4a34b1"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>A scalable mixed-level approach to dynamic analysis of C and C++ programs</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2006</PublicationDate> <Authors> <Author> <DisplayName>Guo, Philip Jia</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>This thesis addresses the difficult task of constructing robust and scalable dynamic program analysis tools for programs written in memory-unsafe languages such as C and C++, especially those that are interested in observing the contents of data structures at run time. In this thesis, I first introduce my novel mixed-level approach to dynamic analysis, which combines the advantages of both source- and binary-based approaches. Second, I present a tool framework that embodies the mixed-level approach. This framework provides memory safety guarantees, allows tools built upon it to access rich source- and binary-level information simultaneously at run time, and enables tools to scale to large, real-world C and C++ programs on the order of millions of lines of code. Third, I present two dynamic analysis tools built upon my framework - one for performing value profiling and the other for performing dynamic inference of abstract types - and describe how they far surpass previous analyses in terms of scalability, robustness, and applicability. Lastly, I present several case studies demonstrating how these tools aid both humans and automated tools in several program analysis tasks: improving human understanding of unfamiliar code, invariant detection, and data structure repair.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>