In order to analyze and test the component-based web application and decide when to stop the testing process, the concept of coverage criteria and test requirement reduction approach are proposed. First, four adequacy...In order to analyze and test the component-based web application and decide when to stop the testing process, the concept of coverage criteria and test requirement reduction approach are proposed. First, four adequacy criteria are defined and subsumption relationships among them are proved. Then, a translation algorithm is presented to transfer the test model into a web application decision-to-decision graph（WADDGraph）which is used to reduce testing requirements. Finally, different sets of test requirements can be generated from WADDGraph by analyzing subsumption and equivalence relationships among edges based on different coverage criteria, and testers can select different test requirements according to different testing environments. The case study indicates that coverage criteria follow linear subsumption relationships in real web applications. Test requirements can be reduced more than 55% on average based on different coverage criteria and the size of test requirements increases with the increase in the complexity of the coverage criteria.展开更多
A support vector machine (SVM) forecasting model based on rough set (RS) data preprocess was proposed by combining the rough set attribute reduction and the support vector machine regression algorithm, because there a...A support vector machine (SVM) forecasting model based on rough set (RS) data preprocess was proposed by combining the rough set attribute reduction and the support vector machine regression algorithm, because there are strong complementarities between two models. Firstly, the rough set was used to reduce the condition attributes, then to eliminate the attributes that were redundant for the forecast, Secondly, it adopted the minimum condition attributes obtained by reduction and the corresponding original data to re-form a new training sample, which only kept the important attributes affecting the forecast accuracy. Finally, it studied and trained the SVM with the training samples after reduction, inputted the test samples re-formed by the minimum condition attributes and the corresponding original data, and then got the mapping relationship model between condition attributes and forecast variables after testing it. This model was used to forecast the power supply and demand. The results show that the average absolute error rate of power consumption of the whole society and yearly maximum load are 14.21% and 13.23%, respectively, which indicates that the RS-SVM forecast model has a higher degree of accuracy.展开更多
基金The National Natural Science Foundation of China(No.90818027,60873050)the National High Technology Research andDevelopment Program of China (863 Program) (No.2009AA01Z147)+2 种基金Opening Foundation of State Key Laboratory Software Engineering in Wu-han University(No.SKLSE20080717)Opening Foundation of State KeyLaboratory for Novel Software Technology in Nanjing University(No.ZZ-KT2008F12)the Key Laboratory Foundation of Shanghai Municipal Science and Technology Commission (No.09DZ2272600)
文摘In order to analyze and test the component-based web application and decide when to stop the testing process, the concept of coverage criteria and test requirement reduction approach are proposed. First, four adequacy criteria are defined and subsumption relationships among them are proved. Then, a translation algorithm is presented to transfer the test model into a web application decision-to-decision graph（WADDGraph）which is used to reduce testing requirements. Finally, different sets of test requirements can be generated from WADDGraph by analyzing subsumption and equivalence relationships among edges based on different coverage criteria, and testers can select different test requirements according to different testing environments. The case study indicates that coverage criteria follow linear subsumption relationships in real web applications. Test requirements can be reduced more than 55% on average based on different coverage criteria and the size of test requirements increases with the increase in the complexity of the coverage criteria.
基金Project(70901025) supported by the National Natural Science Foundation of China
文摘A support vector machine (SVM) forecasting model based on rough set (RS) data preprocess was proposed by combining the rough set attribute reduction and the support vector machine regression algorithm, because there are strong complementarities between two models. Firstly, the rough set was used to reduce the condition attributes, then to eliminate the attributes that were redundant for the forecast, Secondly, it adopted the minimum condition attributes obtained by reduction and the corresponding original data to re-form a new training sample, which only kept the important attributes affecting the forecast accuracy. Finally, it studied and trained the SVM with the training samples after reduction, inputted the test samples re-formed by the minimum condition attributes and the corresponding original data, and then got the mapping relationship model between condition attributes and forecast variables after testing it. This model was used to forecast the power supply and demand. The results show that the average absolute error rate of power consumption of the whole society and yearly maximum load are 14.21% and 13.23%, respectively, which indicates that the RS-SVM forecast model has a higher degree of accuracy.
