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.

Phytoremediation of Metal Contamination using Salix (willows)

Abandoned hardrock mines and the resulting Acid Mine Drainage (AMD) are a source of vast, environmental degradation that are toxic threats to plants, animals, and humans. Cadmium (Cd) and lead (Pb) are metal contaminants often found in AMD. In my mine outwash water samples, cadmium and lead concentrations were 19 and 160 times greater than concentrations in control waterways, and 300 and 40 times greater than EPA Aquatic Life Use water quality standards, respectively. I tested the phytoremediation characteristics of three montane willows native to the Rocky Mountains: Salix drummondiana, S. monticola, and S. planifolia. I tested the willows’ accumulation and tolerance characteristics of cadmium and lead contamination. I found that S. drummondiana accumulated more cadmium in stems than both S. monticola and S. planifolia, and that all three willow species accumulated similar concentrations of lead. I found similar trends for leaf accumulation. I also found that S. monticola had a greater growth and tolerance to the lower lead concentrations than high lead concentrations in addition to containing higher field stem concentrations of lead than S. planifolia. Salix planifolia contained nearly 2.5 times greater concentrations of cadmium in field stems than S. drummondiana. Based on my results, S. drummondiana could aid in aboveground accumulation of cadmium polluted watersheds, and S. monticola could aid in aboveground accumulation and tolerance of lead pollution.