Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item A Binary Classifier for Test Case Feasibility Applied to Automatically Generated Tests of Event-Driven Software(2016) Robbins, Bryan Thomas; Memon, Atif; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)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.Item Predictors of Supported Employment for Transitioning Youth with Developmental Disabilities(2010) Simonsen, Monica Lynn; Neubert, Debra A; Special Education; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Individuals with Disabilities Education Act of 2004 requires school systems to plan systematically for the transition from school to post-secondary education and/or employment and include measurable post-school goals in students' IEPs. Schools are required to coordinate activities, such as work experiences, to assist students in meeting their post-school goals. In addition, IDEA 2004 outlines a requirement for states to evaluate their performance on priority indicators including the percent of youth who had IEPs who are working in the community within the first year after exiting school (Indicator 14, IDEA 2004). Although youth with developmental disabilities (DD) typically stay in school longer than their peers and often receive costly long-term funded supports as adults, these students continue to transition to sheltered post-school employment rather than supported employment (paid work in the community). Studies examining the employment outcomes for youth with disabilities and predictors for favorable post-school outcomes proliferate in the field yet little is known about the types of employment outcomes for transitioning youth with developmental disabilities who receive long-term funded supports from community rehabilitation provider agencies (CRPs) or the variables that best predict supported employment outcomes. In this study, CRP staff members were asked to complete a survey on 560 individuals who received state DD funded supports from one of 81 CRPs across one Mid-Atlantic state. The final sample included 338 subjects (60.4% response rate) from 57 CRPs. Only 14.2% of the transitioning youth with DD were in individual supported employment positions in the community. Over one-third of the sample (36.9%) was in other supported work (e.g. enclaves, mobile crews) through a CRP and 57.1% were engaged in unpaid/sheltered or non-work activities at the CRP. Using multinomial logistic regression, five variables were identified as salient predictors of supported employment: Family expressed preference for supported employment, paid work experience during secondary school years, self-management skills, community mobility skills, and race/ethnicity. The findings are particularly meaningful because this is the first study to examine predictor variables that are relevant for transitioning youth with DD, such as typical secondary school experiences (e.g. post-secondary program participation, unpaid work experience) and the outcome variable reflects the spectrum of employment outcomes for individuals receiving funded supports from CRPs.Item Effects of Model Selection on the Coverage Probability of Confidence Intervals in Binary-Response Logistic Regression(2008-07-24) Zhang, Dongquan; Dayton, C. Mitchell; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)While model selection is viewed as a fundamental task in data analysis, it imposes considerable effects on the subsequent inference. In applied statistics, it is common to carry out a data-driven approach in model selection and draw inference conditional on the selected model, as if it is given a priori. Parameter estimates following this procedure, however, generally do not reflect uncertainty about the model structure. As far as confidence intervals are concerned, it is often misleading to report estimates based upon conventional 1−α without considering possible post-model-selection impact. This paper addresses the coverage probability of confidence intervals of logit coefficients in binary-response logistic regression. We conduct simulation studies to examine the performance of automatic model selectors AIC and BIC, and their subsequent effects on actual coverage probability of interval estimates. Important considerations (e.g. model structure, covariate correlation, etc.) that may have key influence are investigated. This study contributes in terms of understanding quantitatively how the post-model-selection confidence intervals perform in terms of coverage in binary-response logistic regression models. A major conclusion was that while it is usually below the nominal level, there is no simple predictable pattern with regard to how and how far the actual coverage probability of confidence intervals may fall. The coverage probability varies given the effects of multiple factors: (1) While the model structure always plays a role of paramount importance, the covariate correlation significantly affects the interval's coverage, with the tendency that a higher correlation indicates a lower coverage probability. (2) No evidence shows that AIC inevitably outperforms BIC in terms of achieving higher coverage probability, or vice versa. The model selector's performance is dependent upon the uncertain model structure and/or the unknown parameter vector θ . (3) While the effect of sample size is intriguing, a larger sample size does not necessarily achieve asymptotically more accurate inference on interval estimates. (4) Although the binary threshold of the logistic model may affect the coverage probability, such effect is less important. It is more likely to become substantial with an unrestricted model when extreme values along the dimensions of other factors (e.g. small sample size, high covariate correlation) are observed.Item Asymptotic Theory for Multiple-Sample Semiparametric Density Ratio Models and Its Application to Mortality Forecasting(2007-10-03) Lu, Guanhua; Kedem, Benjamin; Mathematical Statistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A multiple-sample semiparametric density ratio model, which is equivalent to a generalized logistic regression model, can be constructed by multiplicative exponential distortions of a reference distribution. Distortion functions are assumed to be nonnegative and of a known finite-dimensional parametric form, and the reference distribution is left as nonparametric. The combined data from all the samples are used in the semiparametric large sample problem of estimating each distortion and the reference distribution. The large sample behavior for both the parameters and the unknown reference distribution are studied. The estimated reference cumulative distribution function is proved to converge weakly to a zero-mean Gaussian process, whose covariance structure provides confidence bands for the reference distribution function. A Kolmogorov-Smirnov type statistic for a goodness-of-fit test of the density ratio model is also studied. In the second part, an approach to modeling and forecasting age-specific mortality in the United States is provided. The approach is based on an extension of a class of semiparametric models to time series. The method combines information from several time series and estimates their predictive distributions conditional on past data. The conditional expectation, the most common predictor, is obtained as a by product from the first moment of the predictive distribution. The confidence band of the predictor is obtained by applying the asymptotic results of the semiparametric density ratio model. A comparison of short term prediction is made between the semiparametric method and the well known method of Lee and Carter \cite{LC(1992)}. Judging by the mean square error (MSE) of prediction for all ages, the semiparametric method reduces the overall MSE appreciably.