Huowang Chen

Regression testing for Web applications based on slicing

Web applications have rapid developing speed and changeable user demands, so the regression testing is much important. Since the changed demands result in different versions of Web applications, and the faults usually hiding in the adjusted contents, the regression testing must cover all the related pages. In order to carry through the regression testing quickly and effectively, we make the simplification with the method of slicing. Firstly, we analyze the possible changes in the Web applications and the influences produced by these changes, discussing in the direct-dependent and indirect-dependent way; next, we give the regression testing method based on slicing emphasized on the indirect-dependent among data, i.e., obtaining the dependent set of changed variables by forward and backward search method and generating the testing suits; conclusion remarks and future work are given at last.

QoS-aware Service Composition Based on Tree-Coded Genetic Algorithm

A novel tree-coding genetic algorithms (TGA) is presented for QoS-aware service composition. Since tree-coding schema can carry the information of static model of service workflow, this feature qualifies TGA to make the chromosomes to be encoded and decoded automatically, and keep the medial result for fitness computing. The Tree-coding can also support the services composition re-planning at runtime effectively. The experiment results show that TGA run faster than the one-dimensional coding GA when the optimal result is same, furthermore the algorithm with tree-coding is effective for re-planning.

A browser compatibility testing method based on combinatorial testing

For ensuring the displaying effects of Web applications, it is important to perform compatibility testing for browsers under the different configurations and it is a hard task to test all. So this paper focused on the improvements for browser compatibility testing. Firstly, we introduced the related work. Then we analysed the functional speciality of the current popular browsers, and provided the preconditions to simplify problems. Next we gave an instance and brought forward the single factor covering method and the pair-wise covering design to gain testing suits. Finally, we introduced the assistant tools we have developed and the future work.

A Survey of Fuzzy Decision Tree Classifier Methodology

Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Over the years, additional methodologies have been investigated and proposed to deal with continuous or multi-valued data, and with missing or noisy features. Recently, with the growing popularity of fuzzy representation, some researchers have proposed to utilize fuzzy representation in decision trees to deal with similar situations. This paper presents a survey of current methods for FDT(Fuzzy Decision Tree)designs and the various existing issues. After considering potential advantages of FDT‘s over traditional decision tree classifiers, the subjects of FDT attribute selection criteria, inference for decision assignment, and decision and stopping criteria are discussed. To be best of our knowledge, this is the first overview of fuzzy decision tree classifier.

Test coverage analysis based on program slicing

Coverage analysis is a structural testing technique, which helps to eliminate gaps in a test suite and determines when to stop testing. To compute test coverage, the paper proposes a gradation model, in which different coverage have different ranks, and the test coverage of the upper layer is compute according to the coverage of all the layers from the lowest to current layer and the rank difference. To distinguish the importance of different variables, the paper proposes a new concept - coverage about variables, based on program slicing, and adds powers according to their importance. Thus we can focus on the important variables to obtain higher test coverage. In most case, the coverage obtained by our method is bigger than that obtained by a traditional measure, because the coverage about a variable takes only the codes related into account, and the gradation model takes more factors into consideration when analyzing test coverage.

An Incremental Fuzzy Decision Tree Classification Method for Mining Data Streams

One of most important algorithms for mining data streams is VFDT. It uses Hoeffding inequality to achieve a probabilistic bound on the accuracy of the tree constructed. Gama et al. have extended VFDT in two directions. Their system VFDTc can deal with continuous data and use more powerful classification techniques at tree leaves. In this paper, we revisit this problem and implemented a system fVFDT on top of VFDT and VFDTc. We make the following four contributions: 1) we present a threaded binary search trees (TBST) approach for efficiently handling continuous attributes. It builds a threaded binary search tree, and its processing time for values inserting is O(nlogn), while VFDT`s processing time is O(n2). When a new example arrives, VFDTc need update O(logn)attribute tree nodes, but fVFDT just need update one necessary node.2) we improve the method of getting the best split-test point of a given continuous attribute. Comparing to the method used in VFDTc, it improves fromO(nlogn)to O (n)in processing time. 3) Comparing to VFDTc, fVFDT`s candidate split-test number decrease fromO(n)to O(logn).4)Improve the soft discretization method to be used in data streams mining, it overcomes the problem of noise data and improve the classification accuracy.

An Efficient Classification System Based on Binary Search Trees for Data Streams Mining

Decision tree construction is a well-studied problem in data mining. Recently, there has been much interest in mining data streams. Domingos and Hulten have presented a one-pass algorithm for decision tree constructions. Their system using Hoeffding inequality to achieve a probabilistic bound on the accuracy of the tree constructed. In this paper, we revisit this problem and propose a decision tree classifier system that uses binary search trees to handle numerical attributes. The proposed system is based on the most successful VFDT, and it achieves excellent performance. The most relevant property of our system is an average large reduction in processing time, while keeps the same tree size and accuracy.

A new decision tree classification method for mining high-speed data streams based on threaded binary search trees

One of most important algorithms for mining data streams is VFDT. It uses Hoeffding inequality to achieve a probabilistic bound on the accuracy of the tree constructed. Gama et al. have extended VFDT in two directions. Their system VFDTc can deal with continuous data and use more powerful classification techniques at tree leaves. In this paper, we revisit this problem and implemented a system VFDTt on top of VFDT and VFDTc. We make the following three contributions: 1) we present a threaded binary search trees (TBST) approach for efficiently handling continuous attributes. It builds a threaded binary search tree, and its processing time for values inserting is O(nlogn), while VFDTs processing time is O(n

Fast Mining Maximal Frequent ItemSets Based on FP-Tree

sup2

Parallel algorithm for mining fuzzy association rules

esup). When a new example arrives, VFDTc need update O(logn) attribute tree nodes, but VFDTt just need update one necessary node.2) we improve the method of getting the best split-test point of a given continuous attribute. Comparing to the method used in VFDTc, it improves from O(nlogn) to O (n) in processing time. 3) Comparing to VFDTc, VFDTts candidate split-test number decrease from O(n) to O(logn). Comparing to VFDT, the most relevant property of our system is an average reduction of 25.53% in processing time, while keep the same tree size and accuracy. Overall, the techniques introduced here significantly improve the efficiency of decision tree classification on data streams.