The Superagency Solution

In many parts of the country, hydraulic fracturing has brought energy development onto people’s doorsteps. Efforts by local governments to employ traditional land use mechanisms to study and mitigate some of the impacts of these latest intrusions have erupted into battles over the scope of statewide agencies’ control. Forgotten in this fray are many renewable energy resources. As a general rule, they are not subject to statewide oversight, and consequently renewable energy providers must navigate the myriad of siting and permitting requirements of local jurisdictions. For several years, scholars have urged more statewide renewable energy siting procedures to level the playing field. California is the national leader in renewable energy deployment, yet its statewide energy commission does not have jurisdiction over the siting of photovoltaic solar or wind energy plants. This article explores when statewide siting is beneficial and when it may be contraindicated, making a case for consolidation of all large-scale siting under the purview of California’s “superagency,” the California Energy Commission.

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.