距离与权重相结合的导向式灰盒模糊测试方法

导向式灰盒模糊测试是一种能够快速对程序指定位置进行测试的技术。通过对当前导向式灰盒模糊测试技术导向不够精确的问题进行分析,提出一种新的导向式灰盒模糊测试方法,并引入基本块权重与函数路径长度的概念。通过对被测程序的静态分析,构建被测程序的函数调用图和控制流程图,计算更准确的基本块距离并插桩到被测程序中。在模糊测试时通过插桩追踪并计算每个测试用例到指定目标的距离,模糊测试器依据该距离计算种子能量以实现对目标区域的导向,并基于该方法实现原型系统Afl-guide。实验结果表明,与现有的导向式模糊测试方法相比,该方法对目标区域导向更精确、路径覆盖更广,能够更快地生成覆盖程序指定位置的测试用例。     

软件构件组装基础研究进展

构件组装技术是基于构件软件开发的核心技术。构件组装方法可以分为“黑盒”、“白盒”和“灰盒”3类,“灰盒”方法是研究的重点,其研究主要集中在基于框架的方法、基于连接子的方法和基于胶合代码的方法方面。当前,软件构件组装技术正与其它软件技术的研究相结合,但离实际应用仍存在一定的距离。     

柴油机ECU硬件在环灰盒测试用例开发研究

在柴油机电子控制单元(Electronic Control Unit,ECU)开发过程中,为更好地利用硬件在环(Hard-ware-in-the-Loop,HIL)测试对ECU进行全面深入测试,快速准确地跟踪控制模块的故障,分析了柴油机ECU HIL灰盒测试原理,提出了一种基于灰盒测试原理的柴油机ECU HIL测试用例开发方法.以柴油机ECU控制模块中起动喷油量计算模块为对象进行了方法运用,在实例开发过程中对各开发步骤进行了详细说明与例证,并对开发的灰盒测试用例进行了实验验证.结果 表明,灰盒测试用例能对该模块内关键功能点、参数及外部关键输入输出信号进行测试,实现对该模块的HIL测试.此例验证了基于灰盒测试原理的柴油机ECU HIL用例开发方法的适用性,为V开发模式中保证柴油机ECU质量提供了新的思路与方法.     

HVM Rootkit及其控制粒度的设计与实现

硬件辅助虚拟化技术的出现为Rootkit技术提供了新的研究方向。在介绍Rootkit技术及其发展方向的基础上,设计并实现了基于硬件辅助虚拟化技术的Rootkit（Hardware-assisted Virtual Machine Rootkit,HVM Rootkit）在Windows平台下的原型——MiniHVMR。在此基础上,分析了MiniHVMR的对于客户操作系统（Guest OS）的控制粒度,提出一种基于访问FS寄存器信息的方法,提高了MiniHVMR对Guest OS的控制粒度并给出了具体实现细节。     

有效覆盖引导的定向灰盒模糊测试

定向灰盒模糊测试技术在度量种子对目标执行状态的搜索能力时,除了考虑种子逼近目标代码的程度之外,还需要分析种子对多样化执行状态的发现能力,从而避免陷入局部最优.现有的定向灰盒模糊测试主要根据全程序的覆盖统计来度量种子搜索多样化执行路径的能力.然而,目标执行状态仅依赖于部分程序代码.如果带来新覆盖的种子并未探索到目标状态计算所依赖的新执行状态,其不仅不能扩大种子队列对目标执行状态的搜索能力,而且会诱导测试目标无关的代码和功能,阻碍定向测试向目标代码的收敛.为了缓解该问题,从待发现目标执行状态依赖代码的覆盖统计着手,提出了一种有效覆盖引导的定向灰盒模糊测试方法.利用程序切片技术提取影响目标执行状态计算的代码.通过能量调度（即控制种子后代生成数量）,提升引发该部分代码控制流新覆盖变化的种子能量,降低其他冗余种子的能量,使定向灰盒模糊测试专注于搜索目标相关的执行状态.在测试集上的实验结果显示,该方法显著提升了目标状态发现效率.     

基于网页划分的网站应用程序测试新方法

网站已经成为全球信息发布的重要渠道,确认网站应用程序的质量和可靠性成为软件测试的一项重要任务。现在有关网站应用程序的测试技术大多数是基于模型测试技术,依赖于从网站编码信息中产生模型,这些技术可统称为“白盒测试”。目前没有一种技术应用网页的划分进行测试。本文在“白盒测试”的基础之上,提出了基于网页划分的网站应用程序测试技术和基于网页划分的“白盒测试”技术,并通过实验证明基于网页划分的“白盒测试”技术是测试网站应用程序的较为有效的方法。     

灰盒测试模型的应用与研究

灰盒测试是黑盒测试和白盒测试的完美结合。本文结合实际工程项目,给出了一个灰盒测试模型,并将该模型在大型面向对象系统的测试中进行了应用,从而对模型的有效性做了验证。     

面向对象系统灰盒测试模型的研究与应用

白盒测试是保证高可靠性软件质量的有效手段.由于传统白盒测试成本较高,且具有相对的局限性,对大型可靠性软件实施测试可能并不实用.因此,在实际测试过程中需要使用一套黑盒和白盒相结合的灰盒测试的工程化方法.结合实际项目,给出了一个灰盒测试模型,并将该模型应用于大型面向对象系统的测试中,验证了模型的有效性.     

工业码垛机器人示教盒与PLC通信技术研究

针对工业码垛机器人示教盒与PLC的远程通信难题,设计了以单片机AT89S52为示教盒核心的与PLC进行异步串口通信的软硬件解决方案,提出了示教盒与PLC的通信方法,设计了相关的通信协议,给出了硬件电路图和实例软件流程图;通过MAX232的电平转换,根据机器人示教要求进行了联机实验;通过实验,成功实现了示教盒与PLC的通讯和控制,研究了工业码垛机器人中示教盒与PLC信息传输的问题;实验结果表明:示教盒控制反应迅速、操控效果良好,能够满足工业码垛机器人对示教盒功能的要求。     

基于软件缺陷知识的测试框架研究

通过对软件测试框架的研究,给出了测试框架表述的三层模型,并从研究解决的软件测试问题域出发,提出基于缺陷知识的测试框架,具体阐述了该测试框架的三层模型,并且实现了基于缺陷知识的静态分析和灰盒动态测试技术。该测试框架为后续测试技术的研究提供了整体的理论支持。     

Feedback linearization of discrete-time nonlinear uncertain plants via first-principles-based serial neuro-gray-box models

In this paper, discrete-time control schemes based on feedback linearization of serial gray-box models are considered for partially known nonlinear processes. These techniques combine the benefits of feedback linearization, neural networks, and serial gray-box modeling, which result in larger dynamic operating ranges, better extrapolation properties, and fewer data acquisition efforts in comparison with the corresponding black-box-based schemes. First-principles-based serial gray-box models are classified into invertible and non-invertible structures for training purposes, and an improved approximate feedback linearization scheme based on Taylor series terms of a non-affine gray-box model is proposed. Moreover, an affine gray-box model is developed for applying the exact feedback linearization scheme. Simulation results on a fermentation process show that the proposed methods yield significant improvement in modeling and control performance in comparison with that of the black-box feedback linearization schemes.     

Gray-box modeling for prediction and control of molten steel temperature in tundish

To realize stable production in the steel industry, it is important to control molten steel temperature in a continuous casting process. The present work aims to provide a general framework of gray-box modeling and to develop a gray-box model that predicts and controls molten steel temperature in a tundish (TD temp) with high accuracy. Since the adopted first-principle model (physical model) cannot accurately describe uncertainties such as degradation of ladles, their overall heat transfer coefficient, which is a parameter in the first-principle model, is optimized for each past batch separately, then the parameter is modeled as a function of process variables through a statistical modeling method, random forests. Such a model is termed as a serial gray-box model. Prediction errors of the first-principle model or the serial gray-box model can be compensated by using another statistical model; this approach derives a parallel gray-box model or a combined gray-box model. In addition, the developed gray-box models are used to determine the optimal molten steel temperature in the Ruhrstahl–Heraeus degassing process from the target TD temp, since the continuous casting process has no manipulated variable to directly control TD temp. The proposed modeling and control strategy is validated through its application to real operation data at a steel work. The results show that the combined gray-box model achieves the best performance in prediction and control of TD temp and satisfies the requirement for its industrial application.     

Sandpiper: Black-box and gray-box resource management for virtual machines

Virtualization can provide significant benefits in data centers by enabling dynamic virtual machine resizing and migration to eliminate hotspots. We present Sandpiper, a system that automates the task of monitoring and detecting hotspots, determining a new mapping of physical to virtual resources, resizing virtual machines to their new allocations, and initiating any necessary migrations. Sandpiper implements a black-box approach that is fully OS- and application-agnostic and a gray-box approach that exploits OS- and application-level statistics. We implement our techniques in Xen and conduct a detailed evaluation using a mix of CPU, network and memory-intensive applications. Our results show that Sandpiper is able to resolve single server hotspots within 20 s and scales well to larger, data center environments. We also show that the gray-box approach can help Sandpiper make more informed decisions, particularly in response to memory pressure.     

Gray-box identification of dynamic models for the bleaching operation in a pulp mill

The development and application of gray-box identification techniques for modelling the bleaching operation in a Bleached Chemi-Thermo Mechanical Pulp (BCTMP) mill are explained. The process is characterized as a delay dominant recycle process with significant input nonlinearities. The identification was carried out using routine operating data in which the outputs were measured irregularly. The effects of these characteristics and consequent modifications of the system identification techniques are discussed. The resulting models are being used for online prediction and model-based controller design at the mill with satisfactory performance.     

Monte Carlo calculations of neutron diffusion on the ICL DAP

Monte Carlo techniques are potentially well suited to SIMD architectures, provided that the samples used can be treated in parallel. The standard sampling schemes used for the simulation of neutron diffusion make this impossible; we describe here a revised scheme which has been implemented on the ICL DAP.     

Fault diagnosis for heat pumps with parameter identification and clustering

For reducing the energy consumption of heat pumps, fault detection and diagnosis (FDD) is fundamental. The FDD system presented is based on a gray-box process model, the parameters of which are identified online. The faults are classified from the parameters using clustering methods. Known clustering techniques have been simplified and new “vector clustering” techniques have been developed for classifying gradual faults. The FDD system has been tested in various real applications, for one of which the results are presented in this work. The contribution lies on the application side with a software tool developed for the fully automated training process.     

The impact of intrinsic cognitive load on the effectiveness of graphical help for coherence formation

Learning with multiple representations requires the mental integration of corresponding information. This highly demanding process of coherence formation can, for example, be assisted by inter-representational hyperlinks, which foster the visual search for correspondences. In three studies we analyzed the effectiveness of hyperlinks depending on the amount of intrinsic cognitive load (ICL), which is on the one hand determined by the complexity of the learning task and on the other hand by the learners’ expertise. In the first two studies we varied the complexity of the integration task and found that the hyperlinks were only effective for the less complex task (low ICL). In the third study the amount of ICL has been varied by the level of expertise and the effects supported the previous outcomes: the help again was only effective in conditions of low ICL (higher expertise), whereas with high ICL (lower expertise) it was ineffective.