A more highly automated graphic user interface (GUI) test model, which is based on the event-flow graph, is proposed. In the model, an automation tool is first used to carry out reverse engineering for a GUI test sample so as to obtain the event-flow graph. Then an improved ant colony optimization algorithm and a goal-directed searching approach are adopted to create GUI test sample cases. Moreover, a corresponding prototype system based on Microsoft UI automation framework is developed.

In this study, an integrated two-stage optimization model (ITOM) is developed for the planning of municipal solid waste (MSW) management in the City of Regina, Canada. The ITOM improves upon the existing optimization approaches with advantages in uncertainty reflection, dynamic analysis, policy investigation, and risk assessment. It can help analyze various policy scenarios that are associated with different levels of economic penalties when the promised policy targets are violated, and address issues concerning planning for a cost-effective diversion program that targets on the prolongation of the existing landfill. Moreover, violations for capacity and diversion constraints are allowed under a range of significance levels, which reflect the tradeoffs between system-cost and constraint-violation risk. The modeling results are useful for generating a range of decision alternatives under various environmental, socio-economic, and system-reliability conditions. They are valuable for supporting the adjustment (or justification) of the existing waste-management practices, the long-term capacity planning for the city's waste-management system, and the identification of desired policies regarding waste generation and management.

In this study, a dual-interval vertex analysis (DIVA) method is developed, through incorporating the vertex method within an interval-parameter programming framework. The developed DIVA method can tackle uncertainties presented as dual intervals that exist in the objective function and the left-and right-hand sides of the modeling constraints. An interactive algorithm and a vertex analysis approach are proposed for solving the DIVA model. Solutions under an associated a-cut level can be generated by solving a series of deterministic submodels. They can help quantify relationships between the objective function value and the membership grade, which is meaningful for supporting in-depth analyses of tradeoffs between environmental and economic objectives as well as those between system optimality and reliability. A management problem in terms of regional air pollution control is studied to illustrate applicability of the proposed approach. The results indicate that useful solutions for planning the air quality management practices have been generated. They can help decision makers to identify desired pollution-abatement strategies with minimized costs and maximized environmental efficiencies.

针对目前在图形用户界面（graphic user interface, GUI）自动化测试方法中存在的手工依赖性和测试缺乏准确性等问题，提出了一种改进的GUI自动化测试算法。该算法包括两种基于事件流图的GUI自动化测试用例生成策略：基于蚁群算法的日常冒烟测试用例生成策略和基于宽度优先搜索生成树的深度回归测试用例生成策略。将这两种策略应用于没有考虑分层的GUI 事件流图模型中，得到标准GUI 的测试用例，然后再进行GUI测试。结合Microsoft UI Automation 框架和Visual Studio 2005 开发平台，对该算法进行了实验研究。研究表明：该算法可以提高GUI测试的自动化程度和准确性。

In this study, a robust interval-based minimax-regret analysis (RIMA) method is developed and applied to the identification of optimal water-resources-allocation strategies under uncertainty. The developed RIMAapproachcan address uncertainties with multiple presentations.Moreover, it can be usedfor analyzing all possible scenarios associated with different system costs/benefits and risk levels without making assumptions on probabilistic distributions for random variables. In its solution process, an intervalelement cost/benefit matrix can be transformed into an interval-element regret matrix, such that the decision makers can identify desired strategies based on inexact minimax regret (IMMR) criterion. Moreover, the fuzzy decision space is delimited into a more robust one through dimensional enlargement of the original fuzzy constraints. The developed method is applied to a case study of planning water resources allocation under uncertainty. The results indicate that reasonable solutions have been generated. They can help decision makers identify desired strategies for water-resources allocation with a compromise between maximized system benefit and minimized system-failure risk.