软件复杂性研究综述

“软件危机”的根源在于软件的复杂性,当复杂性超过人们能够控制的程度时,软件项目的失败便是必然.系统地介绍和归纳总结了国内外软件复杂性研究现状,分析了目前研究存在的问题,对未来软件复杂性研究热点方向进行了探讨与展望.     

基于集成研讨厅的供应链复杂性问题研究

复杂性是供应链系统的固有属性,贯穿于供应链实施的全过程,对此问题进行深入研究,对于成功实施供应链至关重要.首先分析了供应链系统复杂性的表现,进而论证了供应链系统实质上属于开放的复杂巨系统范畴,适宜用从定性到定量的集成研讨厅体系来研究,在该方法论的指导下,构建了供应链复杂性问题研究的集成研讨厅体系,并对该体系的具体研究内容进行了分析,丰富了供应链复杂性问题的研究.     

基于边界扫描技术的设计复杂性优化研究

对于边界扫描技术的电路系统,可利用现代优化算法对其进行复杂性和测试性改善度的综合优化。主要以较小的复杂性设计来实现测试性的最大化改善,以提高系统测试性优化和缩短系统的测试时间。应用模拟退火算法和模拟退火遗传算法实现系统的设计复杂性优化,相比于贪婪算法,模拟退火算法和模拟退火遗传算法能得到更好的优化率。     

大数据系统和分析技术综述

首先根据处理形式的不同,介绍了不同形式数据的特征和各自的典型应用场景以及相应的代表性处理系统,总结了大数据处理系统的三大发展趋势;随后,对系统支撑下的大数据分析技术和应用(包括深度学习、知识计算、社会计算与可视化等)进行了简要综述,总结了各种技术在大数据分析理解过程中的关键作用;最后梳理了大数据处理和分析面临的数据复杂性、计算复杂性和系统复杂性挑战,并逐一提出了可能的应对之策.     

大型自由和开源软件进化研究

大型自由和开源软件的各种版本源代码和相关开发信息是软件工程研究者研究开源软件项目的重要数据源.归纳开源软件项目的数据源及数据收集方法,给出Linux、FreeBSD操作系统内核的软件进化的分析方法和相关分析结果.重点分析了系统和主要子系统的复杂性进化趋势及推动进化主要原因,提出了快速测算大型开源软件复杂性的方法.得出系统目前以超线性方式进化,而进化的主要推动力是适应系统硬件资源的进化.     

复杂大系统的集成控制

复杂大系统的集成控制李人厚（西安交通大学系统工程所７１００４９）１引言随着科学技术的发展，生产系统的规模越来越大，形成了复杂的大系统．象电力系统，化工系统，军事指挥系统，柔性制造系统等等都是典型的复杂大系统．所谓复杂性主要是指受控对象模型的不确定性；...     

构件软件的回归测试复杂性度量

基于构件的软件构建方法目前被广泛使用在软件开发中,用于减少软件开发的工程成本和加快软件开发进度.在软件维护过程中,由于构件更新或者新版本的发布,基于构件的系统会受到影响,需要进行回归测试.对于指定的软件修改需求,维护者可以实施不同的修改手段.不同的修改手段会导致不同的回归测试复杂性,这种复杂性是软件维护成本和有效性的重要因素.目前的研究没有强调构件软件的回归测试复杂性问题.基于修改影响复杂性模型和度量,提出一种回归测试的复杂性度量框架.该度量框架包括两个部分:基于图的模型和形式化度量计算.该度量可以有效表示构件软件分别在构件和系统层面的回归测试复杂性因素,可视化地体现复杂性变化.然后根据模型,提出具体的度量计算方式.最后,通过实验研究,针对同一个构件软件的相同修改需求,利用若干个实验组进行独立修改实施,然后比较回归测试的复杂性.实验结果表明,所提出的度量方式是可行和有效的.     

基于排列熵算法的混沌伪随机序列复杂性分析

运用排列熵算法分析了离散混沌系统产生的混沌序列和混沌伪随机序列的复杂性,讨论了混沌系统参数对序列复杂性的影响情况。研究表明：多次粗粒化后得到的混沌伪随机序列保持了原有混沌序列的复杂性特点;与Logistic系统和Henon系统相比,TD-ERCS系统产生的混沌伪随机序列的复杂性大且相对稳定,是一个极具密码学应用价值的安全混沌系统。     

完全p-支配集的参数算法

完全p-支配集是一个著名的NP-难问题,在无线传感网络中被用于构建无线传感节点的自我保护网络.该文主要研究完全p-支配集在DG(Disk Graph)模型及其特殊模型上的参数复杂性及参数算法设计.首先证明完全p-支配集在顶点度受限的UDG(Unit Disk Graph)上仍是NP-难的.为了深入理解完全p-支配集在UDG模型上的难解性根源,利用参数化规约进一步研究了完全p支配集在UDG上的参数复杂性.基于难解性根源的分析,最后利用树分解技术和动态规划技术,针对平面图(一种特殊DG模型)上的完全p-支配集,设计了一个时间为O((2p+2)19.1·√kk3n+n3)的精确算法,其中n为给定实例中的顶点个数,k为问题解的大小.     

一种基于内容的静止图像编码方法

用形状自适应DCT（SA-DCT）算法对PEPG静止图像编码系统进行扩展,提供了一种实用的基于内容的静止图像编码系统。该系统能实现对任意形状的图像进行编码和基于内容的操作。实验结果表明,该静止图像编码方案是可行高效的,且不会有大的复杂性,对于交互多媒体业务具有较大的实用价值。     

Qualitative reasoning about approximations in quantitative modeling

Quantitative models are frequently used to analyze physical systems. A central problem in using quantitative models to reason about physical systems is that the complexity of the reasoning process increases drastically with the size and complexity of the model. Human modelers solve this complexity problem by introducing simplifying approximations. The resultant changes in the model tractability and behavior influence a modeler's choice of approximations. This paper addresses the question of how the behavior of a quantitative model changes when approximations to the model are introduced. We present results that show that model behavior changes in many modeling contexts can be derived by analyzing the model structure, the approximation, and the query to be answered. Our experience with a prototype implementation suggests that the techniques can be useful in the design of modeling support systems     

基于规则获取的Petri网诊断模型设计

通过旋转机械故障诊断规则的获取分析,讨论了利用诊断规则设计Petri网诊断模型的方法.该模型的设计建立在简化的样本集基础上,可有效降低Petri网的复杂程度,避免Petri网在复杂系统建模时出现的组合爆炸问题;针对诊断规则的形式,定义并提出将诊断规则转换为标准基本规则序列,便于模型的程序化设计;同时诊断规则的获取可以有效推广故障诊断范围,使其不仅仅局限于样本集,并能消除样本集中存在的不一致问题带来的影响.通过示例证明了这种方法的可行性与有效性,并给出了相关算法.     

一致性全局快照与稳定属性检测算法研究

文中首先介绍了分布式算法的相关概念和分布式算法的分类，然后根据同步模型和异步模型的特点，分别讨论了两种模型的研究方法，重点研究了异步网络模型中的一致性全局快照与稳定属性检测的问题，详细解释并改进了异步网络模型A算法的终止检测镜像算法，同时分析了算法的时间及通信复杂度。     

Metric-based reasoning about pseudocode design in the partial metrics system

The goal of the PMS project is to produce an environment in which the intelligent online assessment of the design for large-scale ADA programming projects is provided. The focus is on the representation of knowledge about the design process for an individual module. Changes in pseudocode complexity are measured in terms of partial metrics. These metrics can take the designers inferences about the pseudocode program structure into account when assessing module complexity. Next, a model of the stepwise refinement process is given which demonstrates how pseudocode elaboration decisions can be modelled in partial metric terms. Finally, the decisions associated with each refinement step for 17 example refinements taken from the computer science literature are described using partial metrics.     

软件科学中Halstead模型的改进

针对Halstead软件复杂性度量模型中存在的缺陷，提出三种修改方案：加权模型、DC模型和OOM模型。对Halstead模型的修改，对于软件可靠性工程和软件的过程控制工程均有重要的现实意义。另外模型的提出也为进一步研制面向对象软件复杂性度量工具提供了理论依据。     

关系分类的学习界限研究

在关系分类模型的学习过程中,目前还没有类似统计学习理论中学习界限的支撑.研究关系分类的学习界限显得尤为重要,为此,提出了一些适用于关系分类模型的学习界限.首先推导出在模型假设空间有限和无限情况下的学习界限.接着提出一个衡量关系模型关联数据能力的复杂性度量——关系维,并证明了该复杂度和关系模型的生长函数之间的关系,得到有限VC 维和有限关系维下的学习界限.然后分析了该界限可学习和有意义的条件,并对界限的可行性进行了详细的分析.最后分析了基于马尔可夫逻辑网的传统学习界限和关系分类中的学习情况,实验结果表明,所提出的界限能够解释实际关系分类中遇到的一些问题.     

Using neural networks in reliability prediction

It is shown that neural network reliability growth models have a significant advantage over analytic models in that they require only failure history as input and not assumptions about either the development environment or external parameters. Using the failure history, the neural-network model automatically develops its own internal model of the failure process and predicts future failures. Because it adjusts model complexity to match the complexity of the failure history, it can be more accurate than some commonly used analytic models. Results with actual testing and debugging data which suggest that neural-network models are better at endpoint predictions than analytic models are presented     

Parallel implementation of multiple model tracking algorithms

The implementations of the Viterbi algorithm (VA) and the interacting multiple model (IMM) algorithm on a shared-bus and shared-memory multiple-input multiple-data (MIMD) multiprocessor are discussed. The computational complexity as well as the speedup and efficiency are examined in detail. It is shown that the computational complexity of the parallel implementation of these algorithms is about the same in both memory space and processing time categories. Efficiency with P processors is about 1-1/P for small P and is expected to be relatively high for large P, especially when many filters and large state and measurement vectors are considered     

Prozessgetriebenes Datenqualitätsmanagement durch Integration von Datenqualität in bestehende Prozessmodelle

The importance of high data quality and the need to consider data quality in the context of business processes are well acknowledged. Process modeling is mandatory for process-driven data quality management, which seeks to improve and sustain data quality by redesigning processes that create or modify data. A variety of process modeling languages exist, which organizations heterogeneously apply. The purpose of this article is to present a context-independent approach to integrate data quality into the variety of existing process models. The authors aim to improve communication of data quality issues across stakeholders while considering process model complexity. They build on a keyword-based literature review in 74 IS journals and three conferences, reviewing 1,555 articles from 1995 onwards. 26 articles, including 46 process models, were examined in detail. The literature review reveals the need for a context-independent and visible integration of data quality into process models. First, the authors derive the within-model integration, that is, enhancement of existing process models with data quality characteristics. Second, they derive the across-model integration, that is, integration of a data-quality-centric process model with existing process models. Since process models are mainly used for communicating processes, they consider the impact of integrating data quality and the application of patterns for complexity reduction on the models’ complexity metrics. There is need for further research on complexity metrics to improve applicability of complexity reduction patterns. Missing knowledge about interdependency between metrics and missing complexity metrics impede assessment and prediction of process model complexity and thus understandability. Finally, our context-independent approach can be used complementarily to data quality integration focusing on specific process modeling languages.