Thesis (M.S.) -- University of Montana, 2008. / Title from author supplied metadata. Description based on contents viewed on July 6, 2009. Includes bibliographical references.

Thesis (M.S.)--Brigham Young University. Dept. of Civil and Environmental Engineering, 2007. / Includes bibliographical references (p. 31-33).

Thesis (Ph. D.)--University of Alabama at Birmingham, 2009. / Additional advisors: Barrett Bryant, Aniruddha Gokhale, Marjan Mernik, Chengcui Zhang. Description based on contents viewed June 3, 2009; title from PDF t.p. Includes bibliographical references (p. 161-177).

Thesis (M.S. in Computer Science)--Vanderbilt University, May 2006. / Title from title screen. Includes bibliographical references.

Thesis (M. S. in Computer Science)--Vanderbilt University, May 2005. / Title from title screen. Includes bibliographical references.

Current advances in the software development industry are growing more ubiquitous by the day. This has caused for security, not only in the broader sense, but specifically within the design and overall development of software itself, to become all the more important. An evidently prevalent problem in the domain of software development is that software security is not consistently addressed during design, which undermines core security concerns, and leads to the development of insecure software. This research seeks to address this issue via a model for secure software design, which is based on a software design pattern, namely, the Model-View-Controller (MVC) pattern. The use of a pattern to convey knowledge is not a new notion. However, the ability of software design patterns to convey secure software design is an idea worth investigating. Following identification of secure software design principles and concepts, as well as software design patterns, specifically those relating to the MVC pattern, a model was designed and developed. With the MVC pattern argued as being a suitable foundation for the model, the security conscious MVC (SecMVC) combines secure software design principles and concepts into the MVC pattern. Together herewith, the MVC pattern’s components in the MVC Compound pattern, namely: the Observer pattern, the Strategy pattern, and the Composite pattern, have provided further sub-models for less abstraction and greater detail. These sub-models were developed, as a result of the SecMVC model’s evaluation in the validation for this study, an expert review. Argued in the light of similar research methods, the expert review was chosen – along with a process that included the use of two expert participants to validate the SecMVC model. It was determined through the expert review that the SecMVC model is of sufficient utility, quality, and efficacy to constitute research value. The research methodology process followed was design science, in which the SecMVC model, which includes its related sub-models, serves as the artefact and research output of this study. This research study contributes evidence of the feasibility for integrating knowledge into software design patterns. This includes the SecMVC model itself. In addition, it argues for the use of an expert review, as an evaluative research method for such an artifact.

The major motivation for self-adaptive computing systems is the self-adjustment of the software according to a changing environment. Adaptive computing systems can add, remove, and replace their own components in response to changes in the system itself and in the operating environment of a software system. Although these systems may provide a certain degree of confidence against new environments, their structural and behavioral changes should be validated after adaptation occurs at runtime. Testing dynamically adaptive systems is extremely challenging because both the structure and behavior of the system may change during its execution. After self adaptation occurs in autonomic software, new components may be integrated to the software system. When new components are incorporated, testing them becomes vital phase for ensuring that they will interact and behave as expected. When self adaptation is about removing existing components, a predefined test set may no longer be applicable due to changes in the program structure. Investigating techniques for dynamically updating regression tests after adaptation is therefore necessary to ensure such approaches can be applied in practice. We propose a model-driven approach that is based on change propagation for synchronizing a runtime test model for a software system with the model of its component structure after dynamic adaptation. A workflow and meta-model to support the approach was provided, referred to as Test Information Propagation (TIP). To demonstrate TIP, a prototype was developed that simulates a reductive and additive change to an autonomic, service-oriented healthcare application. To demonstrate the generalization of our TIP approach to be instantiated into the domain of up-to-date runtime testing for self-adaptive software systems, the TIP approach was applied to the self-adaptive JPacman 3.0 system. To measure the accuracy of the TIP engine, we consider and compare the work of a developer who manually identifyied changes that should be performed to update the test model after self-adaptation occurs in self-adaptive systems in our study. The experiments show how TIP is highly accurate for reductive change propagation across self-adaptive systems. Promising results have been achieved in simulating the additive changes as well.

Thesis (Ph. D.)--University of Alabama at Birmingham, 2007. / Additional advisors: Marjan Mernik, Jeffrey G. Gray, Alan P. Sprague, Elliot J. Lefkowitz. Description based on contents viewed May 29, 2008; title from title screen. Includes bibliographical references (p. 140-152).

Indiana University-Purdue University Indianapolis (IUPUI) / Domain-specific models powered using domain-specific modeling languages are traditionally created manually by modelers. There exist model intelligence techniques, such as constraint solvers and model guidance, which alleviate challenges associated with manually creating models, however parts of the modeling process are still manual. Moreover, state-of-the-art model intelligence techniques are---in essence---reactive (i.e., invoked by the modeler). This thesis therefore provides two contributions to model-driven engineering research using domain-specific modeling language (DSML). First, it discusses how DSML semantic and constraint can enable proactive modeling, which is a form of model intelligence that foresees model transformations, automatically executes these model transformations, and prompts the modeler for assistance when necessary. Secondly, this thesis shows how we integrated proactive modeling into the Generic Modeling environment (GME). Our experience using proactive modeling shows that it can reduce modeling effort by both automatically generating required model elements, and by guiding modelers to select what actions should be executed on the model.