Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training data was collected and hand-labeled across 7 systems, 95 web pages, and 17,360 elements. A total of 250 test flows were also manually hand-crafted for training purposes. Various machine learning algorithms were evaluated. Results showed that Random Forest classifiers performed well on several web component classification problems. In addition, Long Short-Term Memory neural networks were able to model and generate new valid test flows.

Static analysis tools can aid programmers by reporting potential programming mistakes prior to the execution of a program. Funqual is a static analysis tool that reads C++17 code ``in the wild'' and checks that the function call graph follows a set of rules which can be defined by the user. This sort of analysis can help the programmer to avoid errors such as accidentally calling blocking functions in time-sensitive contexts or accidentally allocating memory in heap-sensitive environments. To accomplish this, we create a type system whereby functions can be given user-defined type qualifiers and where users can define their own restrictions on the call graph based on these type qualifiers. We demonstrate that this tool, when used with hand-crafted rules, can catch certain types of errors which commonly occur in the wild. We claim that this tool can be used in a production setting to catch certain kinds of errors in code before that code is even run.

As was recognized by same of the most reputable physicists of the world such as Galilee and Einstein, the basic laws of physics must inevitably be founded upon invariance principles. Galilean and special relativity stand as historical landmarks that emphasize this message. It's no wonder that the great developments of modern physics (such as those in elementary particle physics) have been keyed upon this concept. The modern formulation of classical mechanics (see Abraham and Marsden [1]) is based upon "qualitative" or geometric analysis. This is primarily due to the works of Poincare. Poincare showed the value of such geometric analysis in the solution of otherwise insoluble problems in stability theory. It seems that the insights of Poincare have proven fruitful by the now famous works of Kolmogorov, Arnold, and Moser. The concepts used in this geometric theory are again based upon invariance principles, or symmetries. The work of Sophus Lie from 1873 to 1893 laid the groundwork for the analysis of invariance or symmetry principles in modern physics. His primary studies were those of partial differential equations. This led him to the study of the theory of transformations and inevitably to the analysis of abstract groups and differential geometry. Here we show same further applications of Lie group theory through the use of transformation groups. We emphasize the use of transformation invariance to find conservation laws and dynamical properties in chemical physics.

<p>This thesis gives a partial answer to the question: “Can type systems detect modeling errors in scientific computing, particularly for inverse problems derived from physical models?” by considering, in detail, the major aspects of inverse problems in Magnetic Resonance Imaging (MRI). We define a type-system that can capture all correctness properties for MRI inverse problems, including many properties that are not captured with current type-systems, e.g., frames of reference. We implemented a type-system in the Haskell language that can capture the errors arising in translating a mathe- matical model into a linear or nonlinear system, or alternatively into an objective function. Most models are (or can be approximated by) linear transformations, and we demonstrate the feasibility of capturing their correctness at the type level using what is arguably the most difficult case, the (discrete) Fourier transformation (DFT). By this, we mean that we are able to catch, at compile time, all known errors in ap- plying the DFT. The first part of this thesis describes the Haskell implementation of vector size, physical units, frame of reference, and so on required in the mathemat- ical modelling of inverse problems without regularization. To practically solve most inverse problems, especially those including noisy data or ill-conditioned systems, one must use regularization. The second part of this thesis addresses the question of defining new regularizers and identifying existing regularizers the correctness of which (in our estimation) can be formally verified at the type level. We describe such Bayesian regularization schemes based on probability theory, and describe a novel simple regularizer of this type. We leave as future work the formalization of such regularizers.</p> / Master of Science (MSc)

<p>The purpose of this research has been to reassess the Ag-Mg system using the CALPHAD technique. Compared with previous assessments, we carry out the optimization by fitting calculations to the original data instead of second-hand information. Moreover, we use a two sub-lattice model and a four sub-lattice model based on compound energy formalism to simulate both first-order and second-order transformations between the FCC phase and the L1₂ phase. Undoubtedly, the CALPHAD technique has achieved a degree of maturity, but its deficiencies are regularly ignored.</p> <p>In this thesis, we develop an interval method based on Kantorovich’s idea to overcome the shortcomings of the CALPHAD technique. Both advantages and disadvantages of the interval method are discussed. We also present an example of the interval approach on thermodynamic optimization of the Ag-Mg melt. The results suggest that this method would be helpful as a pre-optimization tool.</p> / Master of Applied Science (MASc)

Blind and visually impaired students are under-represented in the science, technology, engineering, and mathematics disciplines of higher education and the workforce. This is due primarily to the difficulties they encounter in trying to succeed in mathematics courses. While there are sufficient tools available to create Braille content, including the special Nemeth Braille used in the U.S. for mathematics constructs, there are very few tools to allow a blind or visually impaired student to create his/her own mathematical content in a manner that sighted individuals can use. The software tools that are available are isolated, do not interface well with other common software, and may be priced for institutional use instead of individual use. Instructors are unprepared or unable to interact with these students in a real-time manner. All of these factors combine to isolate the blind or visually impaired student in the study of mathematics. Nemeth Braille is a complete mathematical markup system in Braille, containing everything that is needed to produce quality math content at all levels of complexity. Blind and visually impaired students should not have to learn any additional markup languages in order to produce math content. This work addressed the needs of the individual blind or visually impaired student who must be able to produce mathematical content for course assignments, and who wishes to interact with peers and instructors on a real-time basis to share mathematical content. Two tools were created to facilitate mathematical interaction: a Nemeth Braille editor, and a real-time instant messenger chat capability that supports Nemeth Braille and MathML constructs. In the Visually Impaired view, the editor accepts Nemeth Braille input, displays the math expressions in a tree structure which will allow sub-expressions to be expanded or collapsed. The Braille constructs can be translated to MathML for display within MathType. Similarly, in the Sighted view, math constructs entered in MathType can be translated into Nemeth Braille. Mathematical content can then be shared between sighted and visually impaired users via the instant messenger chat capability. Using Math in the Dark software, blind and visually impaired students can work math problems fully in Nemeth Braille and can seamlessly convert their work into MathML for viewing by sighted instructors. The converted output has the quality of professionally produced math content. Blind and VI students can also communicate and share math constructs with a sighted partner via a real-time chat feature, with automatic translation in both directions, allowing VI students to obtain help in real-time from a sighted instructor or tutor. By eliminating the burden of translation, this software will help to remove the barriers faced by blind and VI students who wish to excel in the STEM fields of study.

Predictive statistical modeling shows promise in accurately predicting academic performance for students enrolled in online programs. This approach has proven effective in accurately identifying students who are at-risk enabling instructors to provide instructional intervention. While the potential benefits of statistical modeling is significant, implementations have proven to be complex, costly, and difficult to maintain. To address these issues, the purpose of this study is to develop a fully integrated, automated predictive modeling system (PMS) that is flexible, easy to use, and portable to identify students who are potentially at-risk for not succeeding in a course they are currently enrolled in. Dynamic and static variables from a student system (edX) will be analyzed to predict academic performance of an individual student or entire class. The PMS model framework will include development of an open-source Web application, application programming interface (API), and SQL reporting services (SSRS). The model is based on knowledge discovery database (KDD) approach utilizing inductive logic programming language (ILP) to analyze student data. This alternative approach for predicting academic performance has several unique advantages over current predictive modeling techniques in use and is a promising new direction in educational research.

Software as a service is an emerging but important aspect of the web. WebServices play a vital role in providing it. Web Services are commonly provided in one of two architectural styles: a "REpresentational State Transfer" (REST), or using the "Simple Object Access Protocol" (SOAP.) Originally most web content was text and small images. But more recent services involve complex data structures including text, images, audio, and video. The task of optimizing data to provide delivery of these structures is a complex one, involving both theoretical and practical aspects. In this thesis work, I have considered two architectures developed in the REST architectural style and tested them on mixes of data types (plain text, image, audio) being retrieved from a file system or database. The payload which carries the actual content of a data transmission process can either be in Extensible Markup Language (XML) or JavaScript Object Notation (JSON). Both of these language notations are widely used. The two architectures used in this thesis work are titled as Scenario 1 and Scenario 2. Scenario 1 proposes two different cases for storing, retrieving and presenting the data via a REST web service. We investigate the question of what is the best way to provide different data types (image, audio) via REST Web Service. Payload size for JSON and XML are compared. Scenario 2 proposes an enhanced and optimized architecture which is derived from the pros of the first two cases in Scenario 1. The proposed architecture is best suited for retrieving and serving non-homogeneous data as a service in a homogenous environment. This thesis is composed of theoretical and practical parts. The theory part contains the design and principles of REST architecture. The practical part has a Web Service provider and consumer model developed in Java. The practical part is developed using the Spring MVC framework and Apache CXF, which provides an implementation using JAX-RS, the Java API for RESTful services. A glossary of acronyms used in this thesis appears in the appendix on page 101.

According to some researchers, a hybrid approach can help optimize the software development lifecycle by combining two or more methodologies. RUP and Scrum are two methodologies that successfully complement each other to improve the software development process. However, the literature has shown only few case studies on exactly how organizations are successfully applying this hybrid methodology and the benefits and issues found during the process. To help fill this literature gap, the main purpose of this thesis is to describe the development of the Lobbyist Registration and Tracking System for the City of Jacksonville case study where a hybrid approach, that integrates RUP and Scrum, was implemented to develop a major application to provide additional empirical evidence and enrich the knowledge in this under-investigated field. The objective of this research was fulfilled since the case study was described in detail with the specific processes implemented using RUP and Scrum within the context of the IBM Rational Collaborative Lifecycle Management Solution. The results may help researchers and practitioners who are looking for evidence about conducting a hybrid approach. However, more case studies that successfully combine RUP and Scrum need to be developed in order to have enough empirical evidence.

The Unified Modeling Language (UML) is the successor to the wave of object-oriented analysis and design (OOA&D) methods that appeared in the late '80s and early '90s. The class diagram is one of the most useful diagrams in UML. The class diagram technique has become truly central within object-oriented methods.