Guangchun Luo

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0950-5849/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.infsof.2011.09.007 ⇑ Corresponding author. Tel.: +86 028 61830557; fa E-mail addresses: may@uestc.edu.cn (Y. Ma), g snow.zeng@gmail.com (X. Zeng), agchen@uestc.edu.c Context: Software defect prediction studies usually built models using within-company data, but very few focused on the(More)
Wireless Mesh Networks (WMNs) are widely used in many areas, such as industrial, commercial and public-safety environments. However, due to the open nature of wireless communication, it is relatively easy for an adversary to launch serious wormhole attack which can’t be even prevented by cryptographic protocols. To enhance the efficiency and facility of(More)
The MVC (Model/View/Controller) design pattern was developed in Smalltalk-80 and widely used in software design. This paper introduces a novel Web application frame based on MVC. This frame separates the transaction logic from the presentation format. It also improves the system maintain-ability, scalability and performance by using the module data-base,(More)
When traditional IDS (Intrusion Detection System) is used to detect and analyze the great flow data transfer in high-speed network, it usually causes the computation bottleneck. This paper presents a new Mobile Agent Distributed IDS (MADIDS) system basing on the mobile agents. This system is specifically designed to process the great flow data transfer in(More)
The goal of this paper is to catalog the software defect prediction using machine learning. Over the last few years, the eld of software defect prediction has been extensively studied because of it's crucial position in the area of software reliability maintenance, software cost estimation and software quality assurance. An insurmountable problem associated(More)
Software defect prediction is to predict the defect-prone modules for the next release of software or cross project software. Real world data mining applications, including software defect prediction domain, must address the issue of learning from imbalanced data sets. As pointed out by Khoshgoftaar et al. [1] and Menzies et al. [2], the majority of defects(More)
Traffic flow cannot be predicted solely based on historical data due to its high dynamics and sensitivity to emergency situations. In this paper, a real traffic data collected from 2011 to 2014 is used, and an adaptive prediction model based on a variant of Extreme Learning Machine (ELM), namely On-line Sequential ELM with forgetting mechanism, is built.(More)