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.

Effects of Web-Based Self-Reporting: College Students’ Self-Efficacy Regarding Fruit and Vegetable Intake

This study evaluated the effect of an online diet-tracking tool on college students’ self-efficacy regarding fruit and vegetable intake. A convenience sample of students completed online self-efficacy surveys before and after a six-week intervention in which they tracked dietary intake with an online tool. Group one (n=22 fall, n=43 spring) accessed a tracking tool without nutrition tips; group two (n=20 fall, n=33 spring) accessed the tool and weekly nutrition tips. The control group (n=36 fall, n=60 spring) had access to neither. Each semester there were significant changes in self-efficacy from pre- to post-test for men and for women when experimental groups were combined (p<0.05 for all); however, these changes were inconsistent. Qualitative data showed that participants responded well to the simplicity of the tool, the immediacy of feedback, and the customized database containing foods available on campus. Future models should improve user engagement by increasing convenience, potentially by automation.