A Binary Classifier for Test Case Feasibility Applied to Automatically Generated Tests of Event-Driven Software

Modern software application testing, such as the testing of software driven by graphical user interfaces (GUIs) or leveraging event-driven architectures in general, requires paying careful attention to context. Model-based testing (MBT) approaches first acquire a model of an application, then use the model to construct test cases covering relevant contexts. A major shortcoming of state-of-the-art automated model-based testing is that many test cases proposed by the model are not actually executable. These \textit{infeasible} test cases threaten the integrity of the entire model-based suite, and any coverage of contexts the suite aims to provide. In this research, I develop and evaluate a novel approach for classifying the feasibility of test cases. I identify a set of pertinent features for the classifier, and develop novel methods for extracting these features from the outputs of MBT tools. I use a supervised logistic regression approach to obtain a model of test case feasibility from a randomly selected training suite of test cases. I evaluate this approach with a set of experiments. The outcomes of this investigation are as follows: I confirm that infeasibility is prevalent in MBT, even for test suites designed to cover a relatively small number of unique contexts. I confirm that the frequency of infeasibility varies widely across applications. I develop and train a binary classifier for feasibility with average overall error, false positive, and false negative rates under 5\%. I find that unique event IDs are key features of the feasibility classifier, while model-specific event types are not. I construct three types of features from the event IDs associated with test cases, and evaluate the relative effectiveness of each within the classifier. To support this study, I also develop a number of tools and infrastructure components for scalable execution of automated jobs, which use state-of-the-art container and continuous integration technologies to enable parallel test execution and the persistence of all experimental artifacts.

Aging African American Women and the Impact of Positive Psychological Factors on Quality of Life

Aging African-American women are disproportionately affected by negative health outcomes and mortality. Life stress has strong associations with these health outcomes. The purpose of this research was to understand how aging African American women manage stress. Specifically, the effects of coping, optimism, resilience, and religiousness as it relates to quality of life were examined. This cross-sectional exploratory study used a self-administered questionnaire and examined quality of life in 182 African-American women who were 65 years of age or older living in senior residential centers in Baltimore using convenience sampling. The age range for these women was 65 to 94 years with a mean of 71.8 years (SD = 5.6). The majority (53.1%) of participants completed high school, with 23 percent (N = 42) obtaining college degrees and 19 percent (N = 35) holding advanced degrees. Nearly 58 percent of participants were widowed and 81 percent were retired. In addition to demographics, the questionnaire included the following reliable and valid survey instruments: The Brief Cope Scale (Carver, Scheier, & Weintraub, 1989), Optimism Questionnaire (Scheier, Carver, & Bridges, 1994), Resilience Survey (Wagnild & Young, 1987), Religiousness Assessment (Koenig, 1997), and Quality of Life Questionnaire (Cummins, 1996). Results revealed that the positive psychological factors examined were positively associated with and significant predictors of quality of life. The bivariate correlations indicated that of the six coping dimensions measured in this study, planning (r=.68) was the most positively associated with quality of life. Optimism (r=.33), resilience (=.48), and religiousness (r=.30) were also significantly correlated with quality of life. In the linear regression model, again the coping dimension of planning was the best predictor of quality of life (beta = .75, p <.001). Optimism (beta = .31, p <.001), resilience (beta = .34, p, .001) and religiousness (beta = .17, p <.01) were also significant predictors of quality of life. It appears as if positive psychology plays an important role in improving quality of life among aging African-American women.