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.

The introduction of Java 5.0 came along with an extension of the language syntax. Several new language features as generic types and enumeration types were added to the language specification. These features cause downward-incompatibilities, code written in Java 5.0 will not work on older versions of the Java runtime environment. For some active projects, however, it is not possible to upgrade to higher Java versions, since some code might not be supported on Java 5.0. If one still wants to use components written in Java 5.0, these must be downgraded. Up to now this has been accomplished mostly by transforming the byte code of these programs. In this thesis, we present a set of transformations which transform Java 5.0 source code to Java 1.4 compatible code. We successfully apply these transformations to two larger projects and compare our approach to the up to now common byte-code based tools.

Source code plagiarism detection tools have been readily available for use in academia and industry for a long time. While there are many motivations for the usage of these tools in Computer Science education, they are often not implemented on an institutional level. This thesis will describe an efficient process of using source code plagiarism detec-tion tools in academia today. A literature review and a survey will be used to describe the process of finding source code plagiarism. A survey will collect some attitudes of instructors that affect the qualitative concept of efficiency. An interview with respondents using a tool integrated in a learning management system will collect more attitudes regarding efficiency. The administration of source code plagiarism detection tools will be analyzed. The process can be divided into collection, analysis, confirmation, and investigation. Some stages can be fully or partly automated with human intervention. Without integration into a learning management system, the efficiency already gained in the collection stage by using a learning management system will be lost. Some respondents were satisfied with the process of finding source code plagiarism without using any tool. The motivations of using plagiarism detection tools differ among respondents, but they shared positive attitudes of using a tool and using it more often. An efficient process is a university with a plagiarism policy that seeks plagiarism automatically without prior suspicion, using a tool integrated in a learning management system or a repository, with similarity reports of good usability.

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.

No description available.

No description available.

We present a system for the inference of various static properties from source code written in the Maple programming language. We make use of an abstract interpretation framework in the design of these properties and define languages of constraints specific to our abstract domains which capture the desired static properties of the code. Finally we discuss the automated generation and solution of these constraints, describe a tool for doing so, and present some results from applying this tool to several nontrivial test inputs. / Thesis / Master of Science (MSc)

Researchers use datasets of Question-Solution pairs to train machine learning models, such as source code generation models. A Question-Solution pair contains two parts: a programming question and its corresponding Solution Snippet. A Solution Snippet is a source code that solves a programming question. These datasets of Question-Solution pairs can be extracted from a number of different platforms. In this research, I study how Question-Solution pairs are extracted from Stack Overflow (SO). There are two limitations of datasets of Question-Solution pairs extracted from SO: (1) according to the authors of these datasets, some Question-Solution pairs contain Solution Snippets that do not solve the question correctly, and (2) these datasets do not contain the information on how Solution Snippets need to be reused, and such information would enhance the reusability of Solution Snippets. These limitations of datasets of pairs could adversely affect the quality of the code being generated by machine learning models. In this research, I conducted a qualitative study to categorize various presentations of Solution Snippets in SO’s answers as well as how Solution Snippets can be adapted for reuse. By doing so, I identified eight categories of how Solution Snippets are presented in SO’s answers and five categories of how Solution Snippets could be adapted. Based on these results, I concluded several potential reasons why it is not easy to create datasets of Question-Solution pairs. The first categorization informs that finding the correct location of the Solution Snippet is challenging when there are several code blocks within the answer to the question. Subsequently, the researcher must identify which code within that code block is the Solution Snippet. The second categorization informs that most Solution Snippets appear challenging to be adapted for reuse, and how Solution Snippets are potentially adapted is not explicitly stated in them. These insights shed light on creating better quality datasets from questions and answers posted on Stack Overflow. / Graduate