基于Bayes方法的软件运行剖面建模

软件运行剖面对于软件可靠性和软件测试具有重要意义,关于软件运行剖面建模,已提出多种方法。但是这些方法都未考虑先验信息,在现场测试数据匮乏的情况下,将这些先验信息丢弃不用,难以对实际运行剖面作出适当的估计。论文讨论了基于Bayes方法的软件运行剖面建模,该方法能够把先验信息与实际测试信息很好地结合起来,并给出了相应的算法。经仿真验证,基于Bayes方法的软件运行剖面建模方法能够较好地估计软件的实际运行剖面。     

基于正交试验设计的软件操作剖面构造方法

为了解决传统的操作数量控制方法中出现的问题,提出一种基于正交试验设计的软件操作剖面构造方法.该方法首先确定操作额定值,接着建立一个粗略的操作剖面,然后在一些粗略操作对应的多属性区域内通过比较发生概率和正交试验设计等方法选取新的操作,最后整理操作及发生概率,得到最终的操作剖面.应用该方法能够开发出满足要求的操作剖面,并分析指出了该方法的优越性.     

基于操作概图的软件系统测试

介绍了操作概图的基本概念及其列表表示法和图示表示法,提出了操作概图的多级列表表示法.说明了操作概图的开发方法,包括操作发起者标识、表示方法选择、操作列表创建和发生概率确定.讨论了基于操作概图的软件测试方法,主要包括测试用例数确定、测试用例数分配、测试用例确定和失效密度分析.     

The automatic generation of load test suites and the assessment ofthe resulting software

Three automatic test case generation algorithms intended to test the resource allocation mechanisms of telecommunications software systems are introduced. Although these techniques were specifically designed for testing telecommunications software, they can be used to generate test cases for any software system that is modelable by a Markov chain provided operational profile data can either be collected or estimated. These algorithms have been used successfully to perform load testing for several real industrial software systems. Experience generating test suites for five such systems is presented. Early experience with the algorithms indicate that they are highly effective at detecting subtle faults that would have been likely to be missed if load testing had been done in the more traditional way, using hand-crafted test cases. A domain-based reliability measure is applied to systems after the load testing algorithms have been used to generate test data. Data are presented for the same five industrial telecommunications systems in order to track the reliability as a function of the degree of system degradation experienced     

Hypoglycaemia detection using fuzzy inference system with multi-objective double wavelet mutation Differential Evolution

In this paper, a fuzzy inference system (FIS) is developed to recognize hypoglycaemic episodes. Hypoglycaemia (low blood glucose level) is a common and serious side effect of insulin therapy for patients with diabetes. We measure some physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus (TIDM) patients. The FIS captures the relationship between the inputs of heart rate (HR), corrected QT interval of the electrocardiogram (ECG) signal (QT_c), change of HR, change of QT_c and the output of hypoglycaemic episodes to perform the classification. An algorithm called Differential Evolution with Double Wavelet Mutation (DWM-DE) is introduced to optimize the FIS parameters that govern the membership functions and fuzzy rules. DWM-DE is an improved Differential Evolution algorithm that incorporates two wavelet-based operations to enhance the optimization performance. To prevent the phenomenon of overtraining (over-fitting), a validation approach is proposed. Moreover, in this problem, two targets of sensitivity and specificity should be met in order to achieve good performance. As a result, a multi-objective optimization using DWM-DE is introduced to perform the training of the FIS. Experiments using the data of 15 children with TIDM (569 data points) are studied. The data are randomly organized into a training set with 5 patients (l99 data points), a validation set with 5 patients (177 data points) and a testing set with 5 patients (193 data points). The result shows that the proposed FIS tuned by the multi-objective DWM-DE can offer good performance of doing classification.     

Gaussian kernel-based fuzzy inference systems for high dimensional regression

We propose a novel architecture for a higher order fuzzy inference system (FIS) and develop a learning algorithm to build the FIS. The consequent part of the proposed FIS is expressed as a nonlinear combination of the input variables, which can be obtained by introducing an implicit mapping from the input space to a high dimensional feature space. The proposed learning algorithm consists of two phases. In the first phase, the antecedent fuzzy sets are estimated by the kernel-based fuzzy c-means clustering. In the second phase, the consequent parameters are identified by support vector machine whose kernel function is constructed by fuzzy membership functions and the Gaussian kernel. The performance of the proposed model is verified through several numerical examples generally used in fuzzy modeling. Comparative analysis shows that, compared with the zero-order fuzzy model, first-order fuzzy model, and polynomial fuzzy model, the proposed model exhibits higher accuracy, better generalization performance, and satisfactory robustness.     

Predicting injection profiles using ANFIS

Decision making pertaining to injection profiles during oilfield development is one of the most important factors that affect the oilfields’ performance. Since injection profiles are affected by multiple geological and development factors, it is difficult to model their complicated, non-linear relationships using conventional approaches. In this paper, two adaptive-network-based fuzzy inference systems (ANFIS) based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition based fuzzy inference system (FIS), named ANFIS-GRID, and (2) subtractive clustering based FIS, named ANFIS-SUB. We compare the performance of resultant FIS and study the effect of parameters. A real-world injection profile data set from the Daqing Oilfield, China is used. FIS are generated and tested using training and testing data from that data set. The impact of data quality on the performance of FIS is also studied. Experiments demonstrate that although soft computing methods are somewhat of tolerant of inaccurate inputs, cleaned data results in more robust models for practical problems. ANFIS-GRID outperforms ANFIS-SUB due to its simplicity in parameter selection and its fitness in the target problem.     

基于运行剖面的测试用例自动生成方法

软件测试是保证软件质量的有效方法,但测试工作过程繁琐,工作量较多,所以探索高效快捷的测试用例生成方法一直是大家追求的目标,随着软件规模的日益增大,这种要求也变得更加迫切;软件运行剖面提供了软件运行的流程及各流程发生的概率,在软件测试的各个阶段对测试用例的生成均具有指导意义;在某嵌入式系统的软件确认测试阶段,剖析运行剖面提供的相关信息,结合生物进化遗传算法,由原始测试用例经过几代的遗传和进化之后,生成符合运行路径覆盖范围及深度要求的测试用例。     

GSM churn management by using fuzzy c-means clustering and adaptive neuro fuzzy inference system

Churn management is important and critical issue for Global Services of Mobile Communications (GSM) operators to develop strategies and tactics to prevent its subscribers to pass other GSM operators. First phase of churn management starts with profile creation for the subscribers. Profiling process evaluates call detail data, financial information, calls to customer service, contract details, market details and geographic and population data of a given state. In this study, input features are clustered by x-means and fuzzy c-means clustering algorithms to put the subscribers into different discrete classes. Adaptive Neuro Fuzzy Inference System (ANFIS) is executed to develop a sensitive prediction model for churn management by using these classes. First prediction step starts with parallel Neuro fuzzy classifiers. After then, FIS takes Neuro fuzzy classifiers’ outputs as input to make a decision about churners’ activities.     

软件可靠性测试中最小测试量的确定方法

分析了软件可靠性测试中确定最小测试量的重要意义,阐述了基于操作剖面的软件可靠性测试数据生成方法和软件可靠性测试数据统计特征原理下的确定最小测试量的理论依据和具体方法,提出了该理论指导下的测试数据生成流程,通过实例给出了方法的具体应用过程.     

软件漏洞模糊测试的关键分支探索及热点更新算法

在软件开发及应用中,由于具有可复现性,模糊测试能够帮助发现漏洞和有针对性地对漏洞成因进行分析。为了解决模糊测试过程的效率及测试力度等问题,提出了软件漏洞模糊测试的关键分支探索及热点更新算法。该方法通过捕获、分析和利用受检程序在处理测试用例时的执行位置的关键信息,以指导模糊测试过程的探索方向和测试用例的生成。实验结果表明,提出的方法相较于传统随机发散的模糊测试方法在漏洞发现能力上有较大提升,在Otfcc、Swftools等14个开源程序中发现了100余个未被公布的漏洞,为模糊测试用于软件漏洞检测提供了新的可靠途径。     

基于使用剖面的软件可靠性测试用例生成方法

为了获得软件的可靠性指标,需要设计操作剖面来仿真用户实际使用软件的情况,并基于操作剖面来进行可靠性测试.传统的基于操作剖面的可靠性测试方法难以适用于航天软件,为了解决此问题,描述了一种具有层次化网络体系结构特征的使用剖面的形式化定义,阐述了构造这种使用剖面的系统化方法,给出了根据使用剖面生成用于软件可靠性测试操作序列的算法,以及基于操作序列生成可靠性测试用例的方法.提出的方法经过实验验证表明是可行的.     

Operational profiles in software-reliability engineering

A systematic approach to organizing the process of determining the operational profile for guiding software development is presented. The operational profile is a quantitative characterization of how a system will be used that shows how to increase productivity and reliability and speed development by allocating development resources to function on the basis of use. Using an operational profile to guide testing ensures that if testing is terminated and the software is shipped because of schedule constraints, the most-used operations will have received the most testing and the reliability level will be the maximum that is practically achievable for the given test time. For guiding regression testing, it efficiently allocates test cases in accordance with use, so the faults most likely to be found, of those introduced by changes, are the ones that have the most effect on reliability     

软件可靠性测试方法探析

在软件可靠性测试中,测试结果会受测试用例的影响。为得到最佳的测试结果,文中提出了基于运行剖面的公平选择测试用例的原则,在此基础上,建立测试用例集,进行软件可靠性测试,目的是对软件的可靠性进行验证。文章最后给出了利用软件的运行剖面和软件失效数据进行软件可靠性评估的方法。     

An Approach to the Modeling of Software Testing with Some Applications

In this paper, an approach to the modeling of software testing is described. A major aim of this approach is to allow the assessment of the effects of different testing (and debugging) strategies in different situations. It is shown how the techniques developed can be used to estimate, prior to the commencement of testing, the optimum allocation of test effort for software which is to be nonuniformly executed in its operational phase. In addition, the question of application of statistical models in cases where the data environment undergoes changes is discussed. Finally, two models are presented for the assessment of the effects of imperfections in the debugging process.     

基于多版本较准的软件可靠性评估

软件可靠性模型都要求测试时的操作剖面与实际运行时的操作剖面一致,但这往往很难达到,造成测试完成之后的可靠性预计与发布之后实际运行中达到的可靠性有较大差距.为了提高软件可靠性评估的准确性,提出了剖面差异性的概念,认为同一软件各个版本的测试操作剖面与实际操作剖面之间的差异性是相同的.在此前提下,提出了一个多版本校准方法,利用软件以前版本的剖面差异性来改进软件当前版本的可靠性评估.     

Interval-valued hesitant fuzzy Einstein prioritized aggregation operators and their applications to multi-attribute group decision making

Interval-valued hesitant fuzzy information aggregation plays an important role in interval-valued hesitant fuzzy set theory, which has received more and more attention in recent years. In this paper, we investigate interval-valued hesitant fuzzy multi-attribute group decision-making problems in which there exists a prioritization relationship among the attributes. Firstly, we introduce some Einstein operational laws on interval-valued hesitant fuzzy sets, and discuss some relations of these operations. Then, we develop two interval-valued hesitant fuzzy prioritized aggregation operators with the help of Einstein operations, such as the interval-valued hesitant fuzzy Einstein prioritized weighted average (IVHFEPWA) operator and the interval-valued hesitant fuzzy Einstein prioritized weighted geometric (IVHFEPWG) operator, whose desirable properties are investigated in detail. We further analyze the relationship between these proposed operators and the existing interval-valued hesitant fuzzy prioritized aggregation operators. Moreover, an approach to interval-valued hesitant fuzzy multi-attribute group decision making is given on the basis of the proposed operators. Finally, a numerical example is provided to demonstrate their effectiveness.     

Issues that support the creation of ICT workarounds: towards a theoretical understanding of feral information systems

enterprise system (ES) software is often supplemented independently by end users who develop personal solutions that establish connections between the required business practices and the ES. One solution is a type of information and communications technology workaround, a feral information system (FIS) – defined as a workaround designed to achieve certain requirements by using any information technology tool that an end user uses in conjunction with, or instead of, the mandated information system. To explore this, we conducted a case study at a large utility company. We conclude that feral information systems are not a behavioural deviance. FISs are by‐products of end users seeking operational efficiency, namely, to nullify additional transactional costs imposed by the ES. Our findings suggest that end users of ES will fall into one of four modes of operation: mode 1, where end users submit to the ES; mode 2, where they dismiss the ES; mode 3, where they develop an FIS that remains hidden from the ES proponents; and mode 4, where they operate the FIS in open defiance of the ES proponents. In this research, we deliberately take the practitioner view and, therefore, outline how different pressures help to create an FIS as a response to a poorly mandated ES. We also make a theoretical contribution by exploring issues that lead to workarounds and suggest that future research into these modes of operation can be theorized in future proposed studies.     

Visual analysis of quality-related manufacturing data using fractal geometry

Improving manufacturing quality is an important challenge in various industrial settings. Data mining methods mostly approach this challenge by examining the effect of operation settings on product quality. We analyze the impact of operational sequences on product quality. For this purpose, we propose a novel method for visual analysis and classification of operational sequences. The suggested framework is based on an Iterated Function System (IFS), for producing a fractal representation of manufacturing processes. We demonstrate our method with a software application for visual analysis of quality-related data. The proposed method offers production engineers an effective tool for visual detection of operational sequence patterns influencing product quality, and requires no understanding of mathematical or statistical algorithms. Moreover, it enables to detect faulty operational sequence patterns of any length, without predefining the sequence pattern length. It also enables to visually distinguish between different faulty operational sequence patterns in cases of recurring operations within a production route. Our proposed method provides another significant added value by enabling the visual detection of rare and missing operational sequences per product quality measure. We demonstrate cases in which previous methods fail to provide these capabilities.