Xiaozhen Xue

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Testing is the most time consuming and expensive process in the software development life cycle. In order to reduce the cost of regression testing, we propose a test case classification methodology based on k-means clustering with the purpose of classifying test cases into two groups of effective and non-effective test cases. The clustering strategy is(More)
Group work is widely used in tertiary institutions due to the considerable advantages of collaborative learning. Previous studies indicated that the group diversity had positive influence on the group work achievement. Therefore, how to achieve diversity within a group effectively and automatically is an interesting question. In this paper we propose a(More)
Although empirical studies have demonstrated the usefulness of statistical fault localizations based on code coverage, the effectiveness of these techniques may be deteriorated due to the presence of some undesired circumstances such as the existence of coincidental correctness where one or more passing test cases exercise a faulty statement and thus(More)
The effectiveness of coverage-based fault localizations in the presence of multiple faults has been a major concern for the software testing research community. A commonly held belief is that the fault localization techniques based on coverage statistics are less effective in the presence of multiple faults and their performance deteriorates. The fault(More)
Due to the complex causality of failure and the special characteristics of test cases, the faults in GUI (Graphic User Interface) applications are difficult to localize. This paper adapts feature selection algorithms to localize GUI-related faults in a given program. Features are defined as the subsequences of events executed. By employing statistical(More)
Intrusion detection is one of the most essential factors for security infrastructures in network environments, and it is widely used in detecting, identifying and tracking the intruders. To solve the drawback of the SVM algorithm to meet the requirements of the network intrusion detection, we propose network intrusion detection based on improved proximal(More)
Internet traffic classification based on flow statistics using machine learning method has attracted great attention. To solve the drawback of the fuzzy K-Means clustering algorithm to meet the requirements of the Internet network classification, we propose Internet traffic classification based on fuzzy kernel K-Means clustering. This method overcomes the(More)
Accurate network traffic classification plays important roles in many areas such as traffic engineering, QoS and intrusion detection etc. Encrypted Peer-to-Peer (P2P) applications have dramatically grown in popularity over the past few years, and now constitute a significant share of the total traffic in many networks. To solve the drawback of the previous(More)