基于LXI的机载制导弹药并行测试方法研究

针对目前弹药测试保障系统测试资源利用率低,测试保障效率无法满足现代化战争需求的的问题,提出一种基于LXI总线的弹药并行测试系统设计思路;该系统应用LXI总线测试系统的分布式测试,集中调度管理的特点;将测试任务分散化,测试能力本地化处理实现了弹药并行测试,有效解决了测试资源闲置以及测试系统保障效率低下的问题;通过并行测试系统设计思路、方案以及关键技术分析为弹药并行测试系统设计提供一种参考思路。     

基于环境资源自动匹配的云测试框架研究与应用

测试人员在软件测试时需要根据测试用例的要求准备合适的测试环境执行,随着大型系统测试环境复杂性的不断增加,测试环境的种类和数量越来越多,一方面测试人员在准备合适的测试环境方面花费了大量的时间,效率低下,另一方面已有的测试环境由于缺乏有效的调度,忙闲不均,利用率不高,容易出现资源浪费。为此提出一种基于环境资源自动匹配的云测试框架,将测试环境资源云化,将测试用例根据对环境资源的需求自动匹配到合适的环境,采用分布式调度执行,返回并合并测试结果。测试人员可以快速按需使用测试环境,同时提高了测试环境的利用率和执行效率。     

MapReduce框架下的朴素贝叶斯算法并行化研究

研究朴素贝叶斯算法MapReduce的并行实现方法, 针对传统单点串行算法在面对大规模数据或者参与分类的属性较多时效率低甚至无力承载大规模运算, 以及难以满足人们处理海量数据的需求等问题, 本文在朴素贝叶斯基本理论和MapReduce框架的基础上, 提出了一种基于MapReduce的高效、廉价的并行化方法. 通过实验表明这种方法在面对大规模数据时能有效提高算法的效率, 满足人们处理海量数据的需求.     

变异策略感知的并行模糊测试研究

模糊测试（fuzzing）具备自动化程度高、可重现性好及易扩展等特点,是软件漏洞挖掘的有效方法之一。针对其固有的测试盲目性和低效性,一批先进的灰盒模糊测试方法被提出并应用在AFL、AFLFast、Vuzzer等工具中。随着高性能芯片和云计算技术的发展,模糊测试可以充分利用其中蕴含的丰富并行计算能力、通过多实例并行的手段进一步提高单位时间内的综合测试效率,典型的代表如Xu等人提出的多核并行方法、谷歌的ClusterFuzz等。但现有并行模糊测试方法,由于不同测试实例在测试用例生成过程中缺少有效的控制,导致生成的畸形样本冗余高、测试综合覆盖率低等问题。针对该问题,本文提出了一种有效控制多测试实例间模糊测试过程的方案,该方案以变异策略为基本粒度进行并行化,定期同步不同测试实例间的有效畸形样本和优化变异策略应用比例,减少不同测试实例间的测试冗余,提高测试综合覆盖率。本文实现了一个变异策略感知的并行模糊测试框架,并选择AFL作为基本模糊测试器,使用5款开源软件及LAVA-M测试集的实验结果表明,相同测试时间内本文的方法比AFL默认调度方法提高目标覆盖率达132%、发现异常数量最多提高50余倍。     

嵌入式软件可靠性仿真测试环境框架

提出了一种针对嵌入式软件进行可靠性测试的仿真测试环境框架。分析了仿真测试环境的系统特性并对其功能组件进行了介绍，对测试环境中的测试运行时组件进行了功能需求的分析，构建出测试运行时组件的框架实现，借助于UML中的抽象类图和顺序图描述对框架的静态特征和动态特征作了介绍，总结了利用框架实现仿真测试环境的优势。     

嵌入式软件可靠性仿真测试环境框架

提出了一种针对嵌入式软件进行可靠性测试的仿真测试环境框架。分析了仿真测试环境的系统特性并对其功能组件进行了介绍，对测试环境中的测试运行时组件进行了功能需求的分析，构建出测试运行时组件的框架实现，借助于UML中的抽象类图和顺序图描述对框架的静态特征和动态特征作了介绍，总结了利用框架实现仿真测试环境的优势。     

一种星载嵌入式软件安全性测试方法

研究了星载嵌入式软件安全性测试问题.提出一种基于故障注入的安全性测试框架,通过构建全数字仿真测试环境,模拟目标系统输入异常故障及运行态异常故障,可以灵活地实现故障注入.根据软件实际运行结果与预期结果的一致性来评价软件检错、容错能力,进而完成对软件的安全性测试及验证过程.     

基于混合人工蜂群算法的并行测试任务优化研究

并行测试技术可以同时进行多个任务的测试,提高资源利用率,节约测试成本;并行测试调度问题是一种复杂的组合优化问题,是并行测试技术的核心要素;并行测试系统作为并行测试技术的载体,自身的性能和求解效率尤其重要;对并行测试完成时间极限定理进行了研究,建立了并行测试任务调度的数学模型,分析了传统元启发式算法求解并行测试问题的不足,提出了基于动态规划的递归搜索技术和人工蜂群算法相结合的混合人工蜂群算法,并采用整数规划精确算法和遗传算法对混合人工蜂群算法进行验证;得出结论采用混合人工蜂群算法进行并行测试任务的调度节约了接近50%的时间,降低了约20%的硬件资源占用,提高了测试效率,可以满足工程实际的应用。     

基于遗传算法的Android系统服务漏洞挖掘

针对常规模糊测试挖掘Android系统服务漏洞效率低的问题,提出并实现基于遗传算法的Anddroid系统服务漏洞挖掘框架ASFuzzer。该框架利用Binder驱动与系统服务的交互向目标发送测试用例。测试过程中根据结果的反馈,引导遗传算法对测试参数不断变异,并提出一种高效的基于概率排序与组合的遗传选择算子模型,从而提高样本覆盖率和模糊测试效率。通过框架在不同系统版本手机上的测试,挖掘到多个系统服务漏洞。与传统模糊测试方法相比,实验结果表明本文方案在漏洞挖掘效率方面更具有优势。     

GPS导航软件的一种功能自动化测试框架

GPS导航软件已经大量涌入市场,对于GPS导航软件本身的质量越来越受到重视。针对GPS导航软件的应用特点,提出了一种GPS导航软件功能自动测试框架,并介绍了相关实现技术,它能自动模拟软件的使用环境和GPS测试数据来验证软件的正确性。在实践中证明此框架行之有效,能有效构建测试环境,及时发现软件的缺陷,并可提高其回归测试的效率。     

Self-Adaptive Resource Management for Large-Scale Shared Clusters

In a shared cluster, each application runs on a subset of nodes and these subsets can overlap with one another. Resource management in such a cluster should adaptively change the application placement and workload assignment to satisfy the dynamic applications workloads and optimize the resource usage. This becomes a challenging problem with the cluster scale and application amount growing large. This paper proposes a novel self-adaptive resource management approach which is inspired from human market: the nodes trade their shares of applications' requests with others via auction and bidding to decide its own resource allocation and a global high-quality resource allocation is achieved as an emergent collective behavior of the market. Experimental results show that the proposed approach can ensure quick responsiveness, high scalability, and application prioritization in addition to managing the resources effectively.     

A simulator for adaptive parallel applications

Dynamically allocating computing nodes to parallel applications is a promising technique for improving the utilization of cluster resources. Detailed simulations can help identify allocation strategies and problem decomposition parameters that increase the efficiency of parallel applications. We describe a simulation framework supporting dynamic node allocation which, given a simple cluster model, predicts the running time of parallel applications taking CPU and network sharing into account. Simulations can be carried out without needing to modify the application code. Thanks to partial direct execution, simulation times and memory requirements are reduced. In partial direct execution simulations, the application's parallel behavior is retrieved via direct execution, and the duration of individual operations is obtained from a performance prediction model or from prior measurements. Simulations may then vary cluster model parameters, operation durations and problem decomposition parameters to analyze their impact on the application performance and identify the limiting factors. We implemented the proposed techniques by adding direct execution simulation capabilities to the Dynamic Parallel Schedules parallelization framework. We introduce the concept of dynamic efficiency to express the resource utilization efficiency as a function of time. We verify the accuracy of our simulator by comparing the effective running time, respectively the dynamic efficiency, of parallel program executions with the running time, respectively the dynamic efficiency, predicted by the simulator under different parallelization and dynamic node allocation strategies.     

A scalable multi-attribute hybrid overlay for range queries on the cloud

Cloud computing has become a promising paradigm as next generation computing model, by providing computation, software, data access, and storage services that do not need to know the location of physical resources interconnected across the globe providing such services. In such an environment, important issues as information sharing and resource/service discovery arise. In order to overcome critical limitations in centralized approaches for information sharing and resource/service discovery, this paper proposes a framework of a scalable multi-attribute hybrid overlay featured with decentralized information sharing, flexible resource/service discovery, fault tolerance and load balancing. Additionally, the proposed hybrid overlay integrates a structured P2P system with an unstructured one to support complex queries. Mechanisms such as load balancing and fault tolerance implemented in our proposed system to improve the overall system performance are also discussed. Experimental results show that the performance of the proposed approach is feasible and stable, as the proposed hybrid overlay improves system performance by reducing the number of routing hops and balancing the load by migrating requests.     

Coordination and control for energy distribution in distributed grid networks: Theory and application to power dispatch problem

This paper presents a new framework considering decentralized energy coordination and generation, and flow control for supply–demand balance in distributed grid networks. Consensus schemes using only local information are employed to produce energy coordination, generation, and flow control signals. For the supply–demand balance, it is required to determine the amount of energy needed at each distributed resource. Also, due to the different generation capacities of each energy resource, coordination of energy flows among distributed energy resources is essentially required. Thus, this paper proposes a new framework which gives decentralized energy coordination scheme, generation, and flow control method considering these constraints based on distributed consensus algorithms. The proposed framework in this paper can be nicely utilized in energy dispatch or energy flow scheduling. Furthermore, it can be applied to various engineering problems including water irrigation systems, traffic networks, and building automation systems since it deals with attributed distribution and resource allocation in large scale distributed systems. Through illustrative examples, the effectiveness of the proposed approaches is illustrated. A possible application to power dispatch problem in the IEEE-14bus is also addressed for more detailed and realistic evaluation.     

基于改进模糊测试的Web应用漏洞挖掘方法

为解决Web模糊测试挖掘漏洞速度较慢、发现漏洞数较少的问题,提出一种改进的Web模糊测试向量生成方法。在通用的Web应用模糊测试结构（Web Fuzzing）基础上,分析现有测试向量生成方法,引入遗传算法来改进Web模糊测试向量生成方法。基于该方法实现XSS模糊测试工具,使用该工具对2个Web应用系统进行测试,将结果与现有模糊测试工具测试结果对比,验证了使用该方法挖掘Web漏洞速度快,发现漏洞数更多,提高了漏洞挖掘效率。     

基于LSTM和动态策略的定向灰盒模糊测试技术

定向模糊测试旨在快速生产测试用例,达到给定的程序目标位置区域并发现程序错误。但目前的定向模糊测试工具普遍存在测试效率较低的问题,为此提出了一种基于神经网络的定向灰盒模糊测试方法,通过学习过去的模糊探索输入文件中不同位置的变异模式以生成模型来预测当前种子能够产生输入增益的位置,从而指导模糊器进行优化突变。同时为了解决定向灰盒模糊器中探索与开发的权衡问题,引入了一种动态策略在模糊测试过程中自适应协调两个阶段。基于现有的模糊测试框架AFL实现了一个原型系统,命名为DYNFuzz,并在3个基准上对其进行了测试和评估,实验结果表明,DYNFuzz具有比其他模糊器更高的定向性能和测试效率,并且不会陷入由探索开发不平衡导致的局部困境。     

移动网格环境下可靠任务调度研究

移动网格的资源环境具有很高的动态性,在任意时刻可能发生资源加入、退出、故障、移动等。采用任务复制策略实现对资源不可靠性的容错。用weibull分布刻画资源的可靠性,建立任务复制模型;形式化描述了基于复制策略的独立任务调度问题,给出调度目标和约束条件;通过遗传算法解决调度问题。仿真结果表明,调度算法具有良好的可扩展性,调度性能与资源可靠性呈线性关系。     

A game-theoretic method of fair resource allocation for cloud computing services

As cloud-based services become more numerous and dynamic, resource provisioning becomes more and more challenging. A QoS constrained resource allocation problem is considered in this paper, in which service demanders intend to solve sophisticated parallel computing problem by requesting the usage of resources across a cloud-based network, and a cost of each computational service depends on the amount of computation. Game theory is used to solve the problem of resource allocation. A practical approximated solution with the following two steps is proposed. First, each participant solves its optimal problem independently, without consideration of the multiplexing of resource assignments. A Binary Integer Programming method is proposed to solve the independent optimization. Second, an evolutionary mechanism is designed, which changes multiplexed strategies of the initial optimal solutions of different participants with minimizing their efficiency losses. The algorithms in the evolutionary mechanism take both optimization and fairness into account. It is demonstrated that Nash equilibrium always exists if the resource allocation game has feasible solutions.