Fail-Safe Testing of Web Applications

This dissertation introduces an approach to generate tests to test fail-safe behavior for web applications. We apply the approach to a commercial web application. We build models for both behavioral and mitigation requirements. We create mitigation tests from an existing functional black box test suite by determining failure type and points of failure in the test suite and weaving required mitigation based on weaving rules to generate a test suite that tests proper mitigation of failures. A genetic algorithm (GA) is used to determine points of failure and type of failure that needs to be tested. Mitigation test paths are woven into the behavioral test at the point of failure based on failure specific weaving rules. A simulator was developed to evaluate choice of parameters for the genetic algorithm. We showed how to tune the fitness function and performed tuning experiments for GA to determine what values to use for exploration weight and prospecting weight. We found that higher defect densities make prospecting and mining more successful, while lower mitigation defect densities need more exploration. We compare efficiency and effectiveness of the approach. First, the GA approach is compared to random selection. The results show that the GA performance was better than random selection and that the approach was robust when the search space increased. Second, we compare the GA against four coverage criteria. The results of comparison show that test requirements generated by a genetic algorithm (GA) are more efficient than three of the four coverage criteria for large search spaces. They are equally effective. For small search spaces, the genetic algorithm is less effective than three of the four coverage criteria. The fourth coverage criteria is too weak and unable to find all defects in almost all cases. We also present a large case study of a mortgage system at one of our industrial partners and show how we formalize the approach. We evaluate the use of a GA to create test requirements. The evaluation includes choice of initial population, multiplicity of runs and a discussion of the cost of evaluating fitness. Finally, we build a selective regression testing approach based on types of changes (add, delete, or modify) that could occur in the behavioral model, the fault model, the mitigation models, the weaving rules, and the state-event matrix. We provide a systematic method by showing the formalization steps for each type of change to the various models.

Investigating and Understanding Student Learning Outcomes in an Online and Face-to-Face Graduate Level Legal Administration Course: An Embedded Mixed Methods Design

Online education is a new teaching and learning medium with few current guidelines for faculty, administrators or students. Its rapid growth over the last decade has challenged academic institutions to keep up with the demand, while also providing a quality education. Our understanding of the factors that determine quality and effective online learning experiences that lead to student learning outcomes is still evolving. There is a lack of consensus on the effectiveness of online versus face-to-face education in the current research. The U.S. Department of Education conducted a meta-analysis in 2009 and concluded that student-learning outcomes in online courses were equal to and, often times, better than face-to-face traditional courses. Subsequent research has found contradictory findings, and further inquiry is necessary. The purpose of this embedded mixed methods design research study is to further our understanding of the factors that create quality and successful educational outcomes in an online course. To achieve this, the first phase of this study measured and compared learning outcomes in an online and in class graduate-level legal administration course. The second phase of the study entailed interviews with those students in both the online and face-to-face sections to understand their perspectives on the factors contributing to learning outcomes. Six themes emerged from the qualitative findings: convenience, higher order thinking, discussions, professor engagement, professor and student interaction, and face-to-face interaction. Findings from this study indicate the factors students perceive as contributing to learning outcomes in an online course are consistent among all students and are supported in the existing literature. Higher order thinking, however, emerged as a stronger theme than indicated in the current research, and the face-to-face nature of the traditional classroom may be more an issue of familiarity than a factor contributing to learning outcomes. As education continues to reach new heights and developments in technology advance, the factors found to contribute to student learning outcomes will be refined and enhanced. These developments will continue to transform the ways in which we deliver and receive knowledge in both traditional and online classrooms. While there is a growing body of research on online education, the field’s evolution has unsettled earlier findings and posed new areas to investigate.