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...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。展开更多
A more automated graphic user interface (GUI) test model, which is based on the event-flow graph, is proposed. In the model, a user interface automation API tool is first used to carry out reverse engineering for a GU...A more automated graphic user interface (GUI) test model, which is based on the event-flow graph, is proposed. In the model, a user interface automation API tool is first used to carry out reverse engineering for a GUI test sample so as to obtain the event-flow graph. Then two approaches are adopted to create GUI test sample cases. That is to say, an improved ant colony optimization (ACO) algorithm is employed to establish a sequence of testing cases in the course of the daily smoke test. The sequence goes through all object event points in the event-flow graph. On the other hand, the spanning tree obtained by deep breadth-first search (BFS) approach is utilized to obtain the testing cases from goal point to outset point in the course of the deep regression test. Finally, these cases are applied to test the new GUI. Moreover, according to the above-mentioned model, a corresponding prototype system based on Microsoft UI automation framework is developed, thus giving a more effective way to improve the GUI automation test in Windows OS.展开更多
为了解决距离基站(Base station,BS)较远的传感器节点使用多跳通信向BS传输数据时产生更高能量消耗和使用寿命短的问题,该文提出了一个多事件节能蚁群优化数据传输(Energy Efficient Ant Colony Optimized Data Transmission,EEACODT)...为了解决距离基站(Base station,BS)较远的传感器节点使用多跳通信向BS传输数据时产生更高能量消耗和使用寿命短的问题,该文提出了一个多事件节能蚁群优化数据传输(Energy Efficient Ant Colony Optimized Data Transmission,EEACODT)无线网络传感器协议.该协议消除了现有协议的一些限制和缺点,根据距离汇聚节点的节点距离将网络划分为多个扇区,根据扇区位置为节点分配特定的活动时间,以此来实现高效节能.该协议采用蚁群优化进行簇间通信,每个簇中簇头选择取决于它到BS的距离和剩余能量,中继节点的选择基于到BS的距离、剩余能量和队列大小这3个条件.实验表明与PSO协议和FAMACROW协议相比,本文EACODT协议在能耗、延时和包传输率方面均优于现有方法和性能.展开更多
文摘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。
文摘A more automated graphic user interface (GUI) test model, which is based on the event-flow graph, is proposed. In the model, a user interface automation API tool is first used to carry out reverse engineering for a GUI test sample so as to obtain the event-flow graph. Then two approaches are adopted to create GUI test sample cases. That is to say, an improved ant colony optimization (ACO) algorithm is employed to establish a sequence of testing cases in the course of the daily smoke test. The sequence goes through all object event points in the event-flow graph. On the other hand, the spanning tree obtained by deep breadth-first search (BFS) approach is utilized to obtain the testing cases from goal point to outset point in the course of the deep regression test. Finally, these cases are applied to test the new GUI. Moreover, according to the above-mentioned model, a corresponding prototype system based on Microsoft UI automation framework is developed, thus giving a more effective way to improve the GUI automation test in Windows OS.
文摘为了解决距离基站(Base station,BS)较远的传感器节点使用多跳通信向BS传输数据时产生更高能量消耗和使用寿命短的问题,该文提出了一个多事件节能蚁群优化数据传输(Energy Efficient Ant Colony Optimized Data Transmission,EEACODT)无线网络传感器协议.该协议消除了现有协议的一些限制和缺点,根据距离汇聚节点的节点距离将网络划分为多个扇区,根据扇区位置为节点分配特定的活动时间,以此来实现高效节能.该协议采用蚁群优化进行簇间通信,每个簇中簇头选择取决于它到BS的距离和剩余能量,中继节点的选择基于到BS的距离、剩余能量和队列大小这3个条件.实验表明与PSO协议和FAMACROW协议相比,本文EACODT协议在能耗、延时和包传输率方面均优于现有方法和性能.
