Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisor: Javier Garcia-Frias, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisor: Javier Garcia-Frias, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Automatically classifying malware variants source code is the most important research issue in the field of digital forensics. By means of malware classification, we can get complete behavior of malware which can simplify the forensics task. In previous researches, researchers use malware binary to perform dynamic analysis or static analysis after reverse engineering. In the other hand, malware developers even use anti-VM and obfuscation techniques try to cheating malware classifiers. With honeypots are increasingly used, researchers could get more and more malware source code. Analyzing these source codes could be the best way for malware classification. In this paper, a novel classification approach is proposed which based on logic and directory structure similarity of malwares. All collected source code will be classified correctly by hierarchical clustering algorithm. The proposed system not only helps us classify known malwares correctly but also find new type of malware. Furthermore, it avoids forensics staffs spending too much time to reanalyze known malware. And the system could also help realize attacker's behavior and purpose. The experimental results demonstrate the system can classify the malware correctly and be applied to other source code classification aspect.

Programmers rely on programming idioms, design patterns, and workaround techniques to express fundamental design not directly supported by the language. Evolving languages often address frequently encountered problems by adding language and library support to subsequent releases. By using new features, programmers can express their intent more directly. As new concerns, such as parallelism or security, arise, early idioms and language facilities can become serious liabilities. Modern code sometimes bene fits from optimization techniques not feasible for code that uses less expressive constructs. Manual source code migration is expensive, time-consuming, and prone to errors. This dissertation discusses the introduction of new language features and libraries, exemplifi ed by open-methods and a non-blocking growable array library. We describe the relationship of open-methods to various alternative implementation techniques. The benefi ts of open-methods materialize in simpler code, better performance, and similar memory footprint when compared to using alternative implementation techniques. Based on these findings, we develop the notion of source code rejuvenation, the automated migration of legacy code. Source code rejuvenation leverages enhanced program language and library facilities by finding and replacing coding patterns that can be expressed through higher-level software abstractions. Raising the level of abstraction improves code quality by lowering software entropy. In conjunction with extensions to programming languages, source code rejuvenation o ers an evolutionary trajectory towards more reliable, more secure, and better performing code. We describe the tools that allow us efficient implementations of code rejuvenations. The Pivot source-to-source translation infrastructure and its traversal mechanism forms the core of our machinery. In order to free programmers from representation details, we use a light-weight pattern matching generator that turns a C like input language into pattern matching code. The generated code integrates seamlessly with the rest of the analysis framework. We utilize the framework to build analysis systems that find common workaround techniques for designated language extensions of C 0x (e.g., initializer lists). Moreover, we describe a novel system (TACE | template analysis and concept extraction) for the analysis of uninstantiated template code. Our tool automatically extracts requirements from the body of template functions. TACE helps programmers understand the requirements that their code de facto imposes on arguments and compare those de facto requirements to formal and informal specifications.

Software maintenance is the lifecycle activity that consumes the greatest amount of resources. Maintenance is a difficult task because of the size of software systems. Much of the time spent on maintenance is spent trying to understand source code. Refactoring offers a way to improve source code design and quality. We present an approach to refactoring that is based on the functionality of source code. Sets of heuristics are captured as patterns of source code. Refactoring opportunities are located using these patterns, and dependencies are verified to check if the located refactorings preserve the dependencies in the source code. Our automated tool performs the functional-based refactoring opportunities detection process, verifies dependencies, and performs the refactorings that preserve dependencies. These refactorings transform the source code into a series of functional regions of code, which makes it easier for developers to locate code they are searching for. This also creates a chunked structure in the source code, which helps with bottom-up program comprehension. Thus, this process reduces the amount of time required for maintenance by reducing the amount of time spent on program comprehension. We perform case studies to demonstrate the effectiveness of our automated approach on two open source applications. / Thesis (Master, Computing) -- Queen's University, 2007-10-05 12:48:56.977

Thesis (Ph.D.)--Drexel University, 2008. / Includes abstract and vita. Includes bibliographical references (leaves 115-117).

Thesis (M.E.E.)--University of Delaware, 2008. / Principal faculty advisor: Javier Garcia-Frias, Dept. of Electrical and Computer Engineering. Includes bibliographical references.

Thesis (M.Sc.). / Written for the School of Computer Science. Title from title page of PDF (viewed 2009/06/18). Includes bibliographical references.

Organizations typically use issue tracking systems (ITS) such as Jira to plan software releases and assign requirements to developers. Organizations typically also use source control management (SCM) repositories such as Git to track historical changes to a code-base. These ITS and SCM repositories contain valuable data that remains largely untapped. As developers churn through an organization, it becomes expensive for developers to spend time determining which software artifact must be modified to implement a requirement. In this work we created, developed, tested and evaluated a tool called Class Change Predictor, otherwise known as CCP, for predicting which class will implement a requirement. Understanding which class will implement a requirement supports several software engineering tasks such as refactoring and assigning requirements to developers. CCP is a data-mining tool operating on top of ITS and SCM repositories which gathers a unique combination of metrics. CCP leverages requirement text to compare current requirements to past requirements and requirements to source code files. CCP performs static analysis on the code-base of each major release of the software artifact. We evaluated CCP on different open source datasets (and the Digital Democracy dataset) by using several machine learning classifiers and pre-processing procedures. Our results show that we can achieve high precision on three out of four datasets. We conclude that accurate class change prediction is feasible, and we propose numerous solutions to increase future accuracy.

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