Predictive models are used to identify potentially problematic components that decrease product quality. Design and source code metrics are used as input features for predictive models; however, there exist a large number of structural measures that capture different aspects of coupling, cohesion, inheritance, complexity and size. An important question to answer is: Which metrics should be used with a model for a particular predictive objective? Identifying a metric subset that improves the performance for the classifier may also provide insights into the structural properties that lead to problematic modules. In this work, a genetic algorithm (GA) is used as a search-based metric selection strategy. A comparative study has been carried out between GA, the Chidamber and Kemerer (CK) metrics suite, and principal component analysis (PCA) as metric selection strategies with different datasets. Program comprehension is important for programmers and the first dataset evaluated uses source code inspections as a subjective measure of cognitively complexity. Predicting the likely location of system failures is important in order to improve a system’s reliability. The second dataset uses an objective measure of faults found in system modules in order to predict fault-prone components. The aim of this research has been to advance the current state of the art in predictive models of software quality by exploring the efficacy of a search-based approach in selecting appropriate metrics subsets. Results show that GA performs well as a metric selection strategy when used with a linear discriminant analysis classifier. When predicting cognitive complex classes, GA achieved an F-value of 0.845 compared to an F-value of 0.740 using PCA, and 0.750 for the CK metrics. By examining the GA chosen metrics with a white box predictive model (decision tree classifier) additional insights into the structural properties of a system that degrade product quality were observed. Source code metrics have been designed for human understanding and program comprehension and predictive models for cognitive complexity perform well with just source code metrics. Models for fault prone modules do not perform as well when using only source metrics and need additional non-source code information, such module modification history or testing history.

The two main challenges of the twenty-first century are the scarcity of energy sources and global warming; trigged by the emission of greenhouse gases. In this context, solar energy became increasingly relevant. Because it makes optimal use of the resources, minimizes environmental impacts, and is sustainable over time. However, before installing solar panels, it is convenient pre-assessing the amount of energy that a building can harvest. This study proposes a methodology to semi-automatically generate information a building scale; on a large area. This thesis integrates airborne Light Detection and Ranging (LiDAR) and WoldView-2 satellite data for modelling the solar energy potential of building rooftops in San Francisco, California. The methodology involved building detection solar potential analysis, and estimations at building scale. First, the outline of building rooftops is extracted using an object-based approach. Next, the solar modelling is carried out using the solar radiation analysis tool in ArcGIS, Spatial Analyst. Then, energy that could potentially be harvested by each building rooftop is estimated. The energy estimation is defined in economic and environmental terms.

Thesis (PhDInformationTechnology)--University of South Australia, 2004.

Thesis (Ph. D.)--George Mason University, 2007. / Title from PDF t.p. (viewed Jan. 21, 2008). Thesis directors: David C. Rine, Elizabeth White. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Vita: p. 230. Includes bibliographical references (p. 222-229). Also available in print.

Thesis (M.S.)--Ohio University, August, 2001. / Title from PDF t.p.

Thesis (Ph. D.)--University of Melbourne, 1992. / Typescript (photocopy). Includes bibliographical references.

Thesis (M.S.)--Auburn University, 2006. / Pages of bibliographic references are misnumbered 46-48 but follow page 48. Abstract. Includes bibliographic references (p.49-51).

Thesis (Ph. D.)--Northeastern University, 2008. / Title from title page (viewed March 3, 2009). College of Computer and Information Science. Includes bibliographical references (p. 195-201).

Thesis (M.Sc.)--University of Adelaide, School of Earth and Environmental Sciences, Discipline of Environmental Biology, 2006. / "January 2006" Bibliography: p. 106-108. Also available in print form.

Thesis (Ph.D.) -- Flinders University, School of Informatics and Engineering. / Typescript (bound). Includes bibliographical references (leaves 167-194). Also availbable electronically.