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Jiang L.-X.,Response Networks | Han W.-J.,Beijing University of Posts and Telecommunications | Yan C.-C.,Beijing University of Posts and Telecommunications | Shi B.-Y.,Beijing University of Posts and Telecommunications
Proceedings - 2012 International Conference on Computer Science and Information Processing, CSIP 2012 | Year: 2012

In this paper, a function testing size estimation model based on testing steps is proposed. The model applies to black boxtesting, and can be used in dependent test as a third party, This model is conducted overall analysis on COCOMO model. Its basic steps include: design test cases, sum total test steps, define the parameters in the model based on the pattern of model, define size factor, and calculate test Size. The model is applied in some test projects. The applications indicate that this model can be used to estimate the size,and bring positive effect to software test. © 2012 IEEE.


Han W.J.,Beijing University of Posts and Telecommunications | Lu T.B.,Beijing University of Posts and Telecommunications | Zhang X.Y.,Beijing University of Posts and Telecommunications | Jiang L.X.,Response Networks | And 2 more authors.
International Journal of Multimedia and Ubiquitous Engineering | Year: 2015

Software effort estimation is the key task for the effective project management. It is widely used for planning and monitoring software project development as a means to deliver the product on time and within budget. So far, no model has been proved to be successful at effectively and accurately estimating software development effort. So it is useful to research a particular model for a particular type of project. This paper present an approach for small organic project, based on our previous work. Besides using Gauss-Newton model to calibrate the parameters of the COCOMO, using Fuzzy logic algorithm to optimize it, we also imply Deming Regression, Expert judgment, and Machine learning to improve this model. This model is based on historical project data. Experimental results show that the model is effective for software estimation. The accuracy comparison of each model is presented. © 2015 SERSC.


Han W.-J.,Beijing University of Posts and Telecommunications | Lu T.-B.,Beijing University of Posts and Telecommunications | Zhang X.-Y.,Beijing University of Posts and Telecommunications | Jiang L.-X.,Response Networks
Journal of Software | Year: 2013

It is very hard to estimate software development effort accurately. So far, no model has proved to be successful at effectively and consistently estimating software development effort or cost. So it is useful to research a particular model for a particular type of project. A new model for small organic project is proposed for software effort estimation. This model is based on actual project data and well-established theories, using Gauss-Newton model to calibrate the parameters of the COCOMO model, using Fuzzy logic models to maintaining the merits of the COCOMO model. In particular, this model has been successfully used in some small project, and has demonstrated great potential to predict software cost more accurately. © 2013 ACADEMY PUBLISHER.


Han W.-J.,Beijing University of Posts and Telecommunications | Jiang L.-X.,Response Networks | Lu T.-B.,Beijing University of Posts and Telecommunications | Zhang X.-Y.,Beijing University of Posts and Telecommunications
International Journal of Multimedia and Ubiquitous Engineering | Year: 2015

Finding defects in a software system is not easy. Effective detection of software defects is an important activity of software development process. In this paper, we propose an approach to predict residual defects, which applies machine learning algorithms (classifiers) and defect distribution model. This approach includes two steps. Firstly, use machine learning Algorithms and Association Rules to get defect classification table, then confirm the defect distribution trend referring to several distribution models. Experiment results on a GUI project show that the approach can effectively improve the accuracy of defect prediction and be used for test planning and implementation. © 2015 SERSC.


Han W.J.,Beijing University of Posts and Telecommunications | Jiang L.X.,Response Networks | Lu T.B.,Beijing University of Posts and Telecommunications | Zhang X.Y.,Beijing University of Posts and Telecommunications
International Journal of Multimedia and Ubiquitous Engineering | Year: 2015

Software Project Management (SPM) is one of the primary factors to software success or failure. Prediction of software development time is the key task for the effective SPM. The accuracy and reliability of prediction mechanisms is also important. In this paper, we compare different machine learning techniques in order to accurately predict the software time. Finally, by comparing the accuracy of different techniques, it has been concluded that Gaussian process algorithm has highest prediction accuracy among other techniques studied. Experimental results show this prediction approach is more effective. © 2015 SERSC.


Han W.J.,Beijing University of Posts and Telecommunications | Jiang L.X.,Response Networks | Zhang X.Y.,Beijing University of Posts and Telecommunications | Sun Y.,Beijing University of Posts and Telecommunications
Applied Mechanics and Materials | Year: 2014

Effective defect prediction is an important topic in software engineering. This paper studies multiple defect prediction models and proposes a defect prediction model during the test period for organic project. This model is based on the analysis of project defect data and refer to Rayleigh model. Defect prediction model plays an important role in the analysis of software quality, rationally allocating resources of software test, improving the efficiency of software test. This paper selected representative software defect data to apply this model, which has been shown to improve project performance. © (2014) Trans Tech Publications, Switzerland.


Han W.,Beijing University of Posts and Telecommunications | Song M.,Beijing University of Posts and Telecommunications | Jiang L.,Response Networks
2011 International Conference on Computer Science and Service System, CSSS 2011 - Proceedings | Year: 2011

A software model of IPECO-PI is established. The model includes three parts of Software Engineering which are Software management, Software development and Software Process Improvement. Every part consists of lots of processes which come from a unified software process library. According to a conceptual framework and a project's characters, a new model can be formed from the IPECO-PI model. So this kind of model is flexible and unified. © 2011 IEEE.


Zheng T.,Response Networks | Zheng Y.,Zhangjiakou Seismic Central Stations
Journal of Natural Disasters | Year: 2015

This article lists the catalog of earthquakes occurring in Chinese mainland in the year 2013 with magnitude greater than 5. 0, and summarizes relevant basic data and damage information of these earthquake events. Combined with earthquake damage assessment reports and conclusions from the earthquake administrations of the events-occurring provinces (and autonomous regions and municipalities), the main data and features of earthquake damage in Chinese mainland in 2013 were summarized. In the end, related data of earthquake damage losses and casualties in Chinese mainland since 1990 were presented and simple comparisons were made.

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