A novel vulnerability prediction model to predict vulnerability loss based on probit regression

Software vulnerability is always an enormous threat to software security. Quantitative analysis of software vulnerabilities is necessary to the evaluation and improvement of software security. Current vulnerability prediction models mainly focus on predicting the number of vulnerabilities regardless of the seriousness of vulnerabilities, therefore these models are unable to reflect the security level of software accurately. Starting from this, we propose a vulnerability prediction model based on probit regression in this paper. Unlike traditional ones, we measure the seriousness of vulnerability by the loss it causes and aim at predicting the accumulative vulnerability loss rather than the number of vulnerabilities. To validate our model, experiment is carried out on two software — Open SSL and Xpdf, and the experimental result shows a good performance of our model.     

Software Maintainability Prediction with UML Class Diagram

Software system can be classified into many function modules from the perspective of user. Unified modeling language( UML) class diagram of each function module was extracted,and design characteristic metrics which influenced software maintainability were selected based on UML class diagram.Choosing metrics of UML class diagram as predictors,and mean maintenance time of function module was regarded as software maintainability parameter. Software maintainability models were built by using back propagation( BP) neural network and radial basis function( RBF) neural network, respectively and were simulated by MATLAB. In order to evaluate the performance of models,the training results were analyzed and compared with leaveone-out cross-validation and model performance evaluation criterion. The result indicated that RBF arithmetic was superior to BP arithmetic in predicting software maintainability.     

A new learning statistic for adaptive filter based on predicted residuals

A key problem for an adaptive filter is to establish a suitable adaptive factor for balancing the contributions of the measurements and the predicted state information from some kinematic models. The reasonable adaptive factor needs a reliable learning statistics to judge the state kinematic model errors. After analyzing the existing two kinds of learning statistics based on the state discrepancy and variance component ratio, a new learning statistic based on predicted residuals is set up, which is different from the exiting learning statistics. The new learning statistic does not need to estimate the kinemetic state parameters before the filtering process, Of course, it does not need necessary measurements to estimate state parameters for all observation epochs. The new learning statistic can be applied together with the learning factor constructed by the state discrepancy. The advantages and shortcomings of the new learning factor are analyzed, and an example is given.     

Evaluation Model of System Survivability

This paper puts forward a survivability evaluation model, SQEM （Survivability Quantitative Evaluation Model）, based on lucubrating the main method existed. Then it defines the measurement factors and parses the survivability mathematically, introduces state change probability and the idea of setting the weights of survivability factors dynamically into the evaluating process of SQEM, which improved the accuracy of evaluation. An example is presented to illustrate the way SQEM works, which demonstrated the validity and feasibility of the method.     

A component-based back-propagation reliability model with low complexity for complex software systems

Since most of the available component-based software reliability models consume high computa- tional cost and suffer from the evaluating complexity for the software system with complex structures, a component-based back-propagation reliability model （CBPRM） with low complexity for the com- plex software system reliability evaluation is presented in this paper. The proposed model is based on the artificial neural networks and the component reliability sensitivity analyses. These analyses are performed dynamically and assigned to the neurons to optimize the reliability evaluation. CBPRM has a linear increasing complexity and outperforms the state-based and the path-based reliability models. Another advantage of CBPRM over others is its robustness. CBPRM depends on the component relia- bilities and the correlative sensitivities, which are independent from the software system structure. Based on the theory analysis and experiment results, it shows that the complexity of CBPRM is evi- dently lower than the contrast models and the reliability evaluating accuracy is acceptable when the software system structure is complex.     

Study on Quality Evaluation Model in Multi-station Assembly Processes

Based on features of dimension variation propagation in multi-station assembly processes, a new quality evaluation model of assembly processes is established. Firstly, the error source of multi-station assembly system is analyzed, the relationship of dimension variation propagation in multi-station assembly processes is studied and the state equation for variation propagation is constructed too. Then, the feature parameters which influence variation propagation and accumulation in multi-station assembly processes are found to evaluate quality characteristic of the assembly system. Through the derivation of equation on dimension variation propagation, station coefficient matrices which are combined and conversed to determine the max eigenvalue are educed. The max eigenvalue is multiplied by the weight coefficient to establish the quality evaluation model in multi-station assembly processes. Furthermore, assembly variation indexes are proposed to judge of the assembly technology process. Finally, through the practical example, the application of the model and assembly variation indexes are presented.     

Model of Next Generation Energy-Efficient Design Software for Buildings

Energy-efficient design for buildings (EEDB) is a vital step towards building energy-saving. In order to greatly improve the EEDB, the next generation EEDB software that makes use of latest technologies needs to be developed. This paper mainly focuses on establishing the model of the next generation EEDB software. Based on the investigation of literatures and the interviews to the designers, the requirements on the next generation EEDB software were identified, where the lifecycle assessment on both energy consumption and environmental impacts, 3D graphics support, and building information modeling (BIM) support were stressed. Then the workflow for using the next generation EEDB software was established. Finally, based on the workflow, the framework model for the software was proposed, and the partial models and the corresponding functions were systematically analyzed. The model lays a solid foundation for developing the next generation EEDB software.     

Model of Security Evaluation of Infrastructure as a Service Layer of Cloud Computing System

At present,most providers of cloud computing mainly provide infrastructures and services of infrastructure as a service(IaaS).But there is a serious problem that is the lack of security standards and evaluation model of IaaS.After analyzing the vulnerabilities performance of IaaS cloud computing system,the mapping relationship was established between the vulnerabilities of IaaS and the nine threats of cloud computing which was released by cloud security alliance(CSA).According to the mapping relationship,a model for evaluating security of IaaS was proposed which verified the effectiveness of the model on OpenStack by the analytic hierarchy process(AHP) and the fuzzy evaluation method.     

Practical Optimal Control of Large-scale Water Distribution Network

According to the network characteristics and actual state of the water supply system in China, the implicit model, which can be solved by the hierarchical optimization method, was established. In special, based on the analyses of the water supply system containing variable-speed pumps, a software has been developed successfully. The application of this model to the city of Hangzhou (China) was compared to experiential strategy. The results of this study showed that the developed model is a promising optimization method to control the large-scale water supply systems.     

Softw are Maintainability Evaluation Based on Software Configuration Management Data

As one of the most important attributes of software quality, software maintainability has been widely recognized.However,the existing maintainability evaluation methods are mostly based on subjectively judgment. Thus it is inapplicable or unbelievable. To evaluate software maintainability objectively,the software configuration management( SCM) data are collected to establish a maintainability model. Based on the hidden Markov chain( HMC), a three-state maintainability estimation model is constructed. To validate the feasibility of the model,a real software example of software maintenance activity is given and the result from the example shows the effectiveness of the proposed method.     

一种软件过程模型的阶梯式重用机制

针对每一个软件项目都建立软件过程模型是比较繁琐和费时的工作,重用已有的软件过程模型不仅能缩短开发周期,也能提高软件的可维护性和可靠性.基于上述思想,提出了一种阶梯式重用方法,给出了形式化描述,以及重用实现算法,最后用一个实例来说明重用方法的执行过程及结果.     

基于程序信息的软件可维护性度量

本文提出一种以量化程序信息为基础,利用灰色评估法对特性的质量进行模糊处理的软件可维护性度量模型,并通过实例得出软件可维护性的定量评价结果,证实该模型和评估方法科学合理。     

基于灰色关联的无人值守变电站维修性评估

分析了模糊评估模型的构建原理,应用模糊映射和灰色关联理论构建了模糊综合评估理论模型,提出了无人值守变电站维修性评估灰色关联度建模和计算方法,给出了220kV无人值守变电站维修性评估实例.结果表明,采用二级分层模型,可以显著降低多因素评估的计算复杂性,高效地实现目标评估.     

从代码重构探讨设计模式

通过一个具体实例的代码重构与几个模式的运用的密切配合，较好地解决了软件系统中的可复用性，可维护性问题。这种基于设计模式的软件构造方法，经作者实际应用证明是可行的和有效的。     

一种基于．NET的4层结构应用模型研究与实现

基于Web的分层模型设计在软件设计开发中已受到人们的广泛关注,已研究和应用了2层、3层结构,并取得了很多应用成果。然而,它不能很好地解决负载不均衡问题,且各层在逻辑上仍不具有独立性,致使应用系统维护困难。提出了一种基于．NET的四层结构应用模型：显示层、业务逻辑层、数据访问层以及数据库,逻辑层次清晰、耦合度低。经研究生综合信息系统开发实践,不仅提高了软件了的质量、增强了可雏护性,而且负载均衡。和3层结构相比,负载能降低24．32％。     

命令模式应用探讨

通过一个具体实例的代码重构与几个模式的运用的密切配合，较好地解决了软件系统中的可复用性，可维护性问题。这种基于设计模式的软件构造方法，经作者实际应用证明是可行的和有效的。     

基于WEKA的软件维护性评价模型的实验研究

为了能够对软件维护性进行评价,以类级软件维护性数据集为例进行了软件维护性实验,应用Logiscope对类的维护性做了定性评价;运用Krakatau Professional收集类各个度量的度量值;通过WEKA得到了类级度量关键属性;最后,选取决策树分类方法训练分类模型,并利用训练好的分类模型对测试集进行测试,得到基本满足要求的类级软件维护性评价模型。     

基于故障树的无人机发动机点火系统维修性建模

结合无人机发动机点火系统的特点,以故障树为基础,建立了无人机发动机点火系统基于故障树的维修性模型,对其进行定量分析得到点火系统的修复性维修时间.该方法利用可靠性分析的故障树理论,提供了维修过程的基本思想和数据,解决了定量分析维修性数据不足的困难,提高了系统维修效率.