ProTest: An Automatic Test Environment for B Specifications

We present ProTest, an automatic test environment for B specifications. B is a model-oriented notation where systems are specified in terms of abstract states and operations on abstract states. ProTest first generates a state coverage graph of a B specification through exhaustive model checking, and the coverage graph is traversed to generate a set of test cases, each being a sequence of B operations. For the model checking to be exhaustive, some transformations are applied to the sets used in the B machine. The approach also works if it is not exhaustive; one can stop at any point in time during the state space exploration and generate test cases from the coverage graph obtained so far. ProTest then simultaneously performs animation of the B machine and the execution of the corresponding implementation in Java, and assigns verdicts on the test results. With some restrictions imposed on the B operations, the whole of the testing process is performed mechanically. We demonstrate the efficacy of our test environment by performing a small case study from industry. Furthermore, we present a solution to the problem of handling non-determinism in B operations.     

基于S分量指数加权H分量的圆形直方图阈值法

HSI颜色空间可以用三维空间下的圆锥模型进行解释,基于HSI颜色空间的H分量圆形直方图实现彩色图像分割具有可行性。针对HSI颜色空间的H分量圆形直方图毛刺较多以及相关阈值选取准则分割精度较低的问题,给出S分量指数加权H分量的直方图公式,利用S分量信息对H分量直方图的毛刺进行平滑处理,并通过分析给出指数加权因子的最优取值。在此基础上,提出一种圆形直方图阈值分割法,通过对整个圆形直方图进行整体角均值计算而得出新的阈值选取准则。实验结果表明,该圆形直方图阈值分割法具有有效性,在测试数据集上,与3个圆形最大熵阈值分割法以及2个阈值分割准则相比,所提方法的像素精度值平均提高3.2%,结构相似度值平均提高2.5%。     

A Study on Acquiring Underlying Behavioral Criteria for Manipulator Motion by Focusing on Learning Efficiency

Conventional humanoid robotic behaviors are directly programmed depending on the programmer's personal experience. With this method, the behaviors usually appear unnatural. It is believed that a humanoid robot can acquire new adaptive behaviors from a human, if the robot has the criteria underlying such behaviors. The aim of this paper is to establish a method of acquiring human behavioral criteria. The advantage of acquiring behavioral criteria is that the humanoid robots can then autonomously produce behaviors for similar tasks with the same behavioral criteria but without transforming data obtained from morphologically different humans every time for every task. In this paper, a manipulator robot learns a model behavior, and another robot is created to perform the model behavior instead of being performed by a person. The model robot is presented some behavioral criteria, but the learning manipulator robot does not know them and tries to infer them. In addition, because of the difference between human and robot bodies, the body sizes of the learning robot and the model robot are also made different. The method of obtaining behavioral criteria is realized by comparing the efficiencies with which the learning robot learns the model behaviors. Results from the simulation have demonstrated that the proposed method is effective for obtaining behavioral criteria. The proposed method, the details regarding the simulation, and the results are presented in this paper.     

On the use of a similarity function for test case selection in the context of model‐based testing

Test case selection in model‐based testing is discussed focusing on the use of a similarity function. Automatically generated test suites usually have redundant test cases. The reason is that test generation algorithms are usually based on structural coverage criteria that are applied exhaustively. These criteria may not be helpful to detect redundant test cases as well as the suites are usually impractical due to the huge number of test cases that can be generated. Both problems are addressed by applying a similarity function. The idea is to keep in the suite the less similar test cases according to a goal that is defined in terms of the intended size of the test suite. The strategy presented is compared with random selection by considering transition‐based and fault‐based coverage. The results show that, in most of the cases, similarity‐based selection can be more effective than random selection when applied to automatically generated test suites. Copyright © 2009 John Wiley & Sons, Ltd.     

Controlling test case explosion in test generation from B formal models

BZ‐TESTING‐TOOLS (BZ‐TT) is a tool set for automated test case generation from B and Z specifications. BZ‐TT uses boundary and cause–effect testing on the basis of the formal model. It has been used and validated on several industrial applications in the domain of critical software, particularly smart card and transport systems. This paper presents the test coverage criteria supported by BZ‐TT. On the one hand, these correspond to various classical structural coverage criteria, but specialized to the case of B abstract machines. The paper gives algorithms for these in Prolog. On the other hand, BZ‐TT introduces new coverage criteria for complex data structures, based on boundary analysis: this paper defines weak and strong state‐boundary coverage, input‐boundary coverage and output‐boundary coverage. Finally, the paper describes how BZ‐TT presents a unified view of these criteria to the validation engineer, and allows him or her to control the test case explosion on a coarse basis (choosing from a range of coverage criteria) as well as a fine basis (selecting options for each state or input variable). Copyright © 2004 John Wiley & Sons, Ltd.     

Goodness of fit tests via exponential series density estimation

The properties of a new nonparametric goodness of fit test are explored. It is based on a likelihood ratio test, applied via a consistent series density estimator in the exponential family. The focus is on its computational and numerical properties. Specifically it is found that the choice of approximating basis is not crucial and that the choice of model dimension, through data-driven selection criteria, yields a feasible, parsimonious procedure. Numerical experiments show that the new tests have significantly more power than established tests, whether based upon the empirical distribution function, or alternate density estimators.     

Predicting EU Energy Industry Excess Returns on EU Market Index via a Constrained Genetic Algorithm

This article introduces an automated procedure to simultaneously select variables and detect outliers in a dynamic linear model using information criteria as objective functions and diagnostic tests as constraints for the distributional properties of errors. A robust scaling method is considered to take into account the sensitiveness of estimates to abnormal data. A genetic algorithm is developed to these purposes. Two examples are presented where models are designed to produce short-term forecasts for the excess returns of the MSCI Europe Energy sector on the MSCI Europe index and a recursive estimation-window is used to shed light on their predictability performances. In the first application the data-set is obtained by a reduction procedure from a very large number of leading macro indicators and financial variables stacked at various lags, while in the second the complete set of 1-month lagged variables is considered. Results show a promising capability to predict excess sector returns through the selection, using the proposed methodology, of most valuable predictors.

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Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.      

Web应用功能交互的建模与测试用例生成

为了产生更有效的测试用例,以Web应用为研究对象,按功能将Web应用划分成若千个功能构件,把Web应用看成是功能构件的集合,针对其相应的交互功能,用FSM对其功能交互行为进行建模.在此基础上提出了完整消息传递序列测试准则,并根据该测试准则生成抽象的测试用例集,以实现对Web应用交互性的测试,提高Web应用测试的有效性,力图实现Web应用测试的自动化.最后设计了一个工具原型用于测试用例的生成.     

基于ASL的模型驱动架构测试方法

针对模型驱动架构（MDA）下的软件开发方法中测试启动比较晚、难以发现隐藏在模型中的缺陷及UML模型描述缺乏精确语义等问题,提出了基于ASL的模型测试方法：从UML模型出发,将动作规约语言（ASL）运用于平台无关模型（PIM）上建立测试模型。给出了ASL语句在MDA过程中的工作原理、平台无关模型的建立过程、测试用例建立的步骤、测试执行的实施过程,最后结合实例,在UML图的基础上运用ASL对测试环境进行构建,并创建了系统的测试用例,对模型及业务逻辑进行了测试。实验结果表明,提出的基于ASL的模型测试方法既能利用模型驱动对复杂的测试用例进行简化和抽象,又能在软件生存期早期揭示软件的缺陷,防止缺陷随着软件开发过程的进行而被放大。     

A consumption system model integrating quality,satisfaction and behavioral intentions in online shopping

A consumption system has two constitutive dimensions: the structural elements and the transaction process. Little attention has been paid to the effect of the consumption system on customer satisfaction and behavioral intentions during online shopping. This paper develops and tests a consumption system model for online shopping that incorporates both product and e-service elements, and the online and offline stages of the transaction process. Applying the transaction process of online shopping, this paper dichotomizes perceived product quality into online perceived product quality (OnPPQ) and offline perceived product quality (OffPPQ), and proposes four dimensions of e-service quality integral to the transaction process. An integrative model is proposed to link OnPPQ, OffPPQ, e-service quality, satisfaction and behavioral intentions. The research was based on empirical data from over 260 university students with experience purchasing apparel from online shops. Structural equation modelling was used to test the model and the hypotheses. The results show that perceived e-service quality, OnPPQ and OffPPQ significantly affect customer satisfaction. However, only customer satisfaction and OffPPQ have a direct effect on behavioral intention. A consumption system model successfully theorizes the dimensions of perceived product quality and e-service quality. The model considers the simultaneous effects of the two structural elements, product and e-service, as well as the temporal effect of the online shopping transaction process. Different structural elements in separate transaction processes have distinct effects on satisfaction and behavioral intentions. The research and its results are especially valuable for online shopping for physical goods.     

面向路径的测试数据自动生成工具的设计

设计了一个通用的基于控制流和数据流的结构测试数据自动生成的工具。该工具根据控制流和数据流测试中所采用的覆盖标准来选取测试路径,并以改进后的迭代松弛法为核心,对所选取的路径生成测试数据。同时工具采用Fibonacci法优化选取路径,对不可达路径进行处理,并对测试数据的分支覆盖率、DCP覆盖率等进行了统计。实验结果表明该工具是可行的。     

Using coverage to automate and improve test purpose based testing

Test purposes have been presented as a solution to avoid the state space explosion when selecting test cases from formal models. Although such techniques work very well with regard to the speed of the test derivation, they leave the tester with one important task that influences the quality of the overall testing process: test purposes have to be formulated manually. In this paper, we present an approach that assists a test engineer with test purpose design in two ways: it allows automatic generation of coverage based test suites and can be used to automatically exercise those aspects of the system that are missed by hand-crafted test purposes. We consider coverage of Lotos specifications, and show how labeled transition systems derived from such specifications have to be extended in order to allow the application of logical coverage criteria to Lotos specifications. We then show how existing tools can be used to efficiently derive test cases and suggest how to use the coverage information to minimize test suites while generating them.     

结构主动控制系统的鲁棒策略

针对目前大多数结构主动控制算法没有考虑结构不确定因素的特点,本文以主动质量阻尼器(AMD:activemass damper)Benchmark结构试验系统为研究对象,提出适合于工程应用的、基于H∞控制理论的主动控制方法.文中建立了主动控制结构的试验系统,根据系统辨识方法得到的面向控制的数学模型,设计YAMD主动控制系统的反馈连接结构,同时对H∞控制权函数的选取以及控制器的设计方法进行了详细的阐述;最后通过试验证明了H∞控制器的有效性和鲁棒性.     

DeepRanger:覆盖制导的深度森林测试方法

深度学习软件的结构特征与传统软件存在明显差异,因此即使展开了大量测试,依然无法有效衡量测试数据对深度学习软件的覆盖情况和测试充分性,并造成后续使用过程中依然可能存在大量未知错误.深度森林是一种新型深度学习模型,其克服了深度神经网络存在的一些缺点,例如:需要大量训练数据、需要高算力平台、需要大量超参数.但目前还没有相关工作对深度森林的测试方法进行研究.针对深度森林的结构特点,制定了一组由随机森林结点覆盖率RFNC、随机森林叶子覆盖率RFLC、级联森林类型覆盖率CFCC和级联森林输出覆盖率CFOC组成的测试覆盖率评价指标.在此基础上,基于遗传算法设计了覆盖制导的测试数据自动生成方法DeepRanger,可自动生成能有效提高模型覆盖率的测试数据集.为对所提出覆盖指标的有效性进行验证,在深度森林开源项目gcForest和MNIST数据集上设计并进行了一组实验.实验结果表明,所提出的4种覆盖指标均能有效评价测试数据集对深度森林模型的测试充分性.此外,与基于随机选择的遗传算法相比,使用覆盖信息制导的测试数据生成方法 DeepRanger能达到更高的模型覆盖率.     

Model selection strategies for identifying most relevant covariates in homoscedastic linear models

A new method in two variations for the identification of most relevant covariates in linear models with homoscedastic errors is proposed. In contrast to many known selection criteria, the method is based on an interpretable scaled quantity. This quantity measures a maximal relative error one makes by selecting covariates from a given set of all available covariates. The proposed model selection procedures rely on asymptotic normality of test statistics, and therefore normality of the errors in the regression model is not required. In a simulation study the performance of the suggested methods along with the performance of the standard model selection criteria AIC, BIC, Lasso and relaxed Lasso is examined. The simulation study illustrates the favorable performance of the proposed method as compared to the above reference criteria, especially when regression effects possess influence of several orders in magnitude. The accuracy of the normal approximation to the test statistics is also investigated; it has been already satisfactory for sample sizes 50 and 100. As an illustration the US college spending data from 1994 is analyzed.     

Extracting semantic indoor maps from occupancy grids

The primary challenge for any autonomous system operating in realistic, rather unconstrained scenarios is to manage the complexity and uncertainty of the real world. While it is unclear how exactly humans and other higher animals master these problems, it seems evident, that abstraction plays an important role. The use of abstract concepts allows us to define the system behavior on higher levels. In this paper we focus on the semantic mapping of indoor environments. We propose a method to extract an abstracted floor plan from typical grid maps using Bayesian reasoning. The result of this procedure is a probabilistic generative model of the environment defined over abstract concepts. It is well suited for higher-level reasoning and communication purposes. We demonstrate the effectiveness of the approach using real-world data.     

多机并发系统中通信模型分层抽象的方法

针对多机并发系统的复杂性,为了辅助用户能从多个角度和层次全面地理解并发系统,就需要逆向产生出能够反映软件系统框架结构的高层架构。基于此本文以进程为边界,提出了一种分层抽取多机并发系统通信模型的方法。此方法基于反射和开放编译的植入机制来获取所需要的动态信息,在此基础上运用分层抽象的策略,分别从系统、节点、进程三个层次对多机并发系统的通信结构和设计结构进行逆向恢复,最后对该方法进行系统的实验研究。结果表明,分层抽象所得到并发系统的通信模型能够正确、有效地反映系统设计时的高层架构关系。     

交互式蚂蚁算法

传统蚁群算法需利用显式评估函数引导算法搜索,因而不适用于那些优化性能指标不能或者难以数量化的系统.为此,提出一种将人对问题解的数量评价值作为目标函数值的交互式蚂蚁算法.从人机交互的特点出发,设计了算法模型的结构、信息素的放置方式与更新策略和用户的评价方式.最后利用模拟算法环境的函数优化实验和汽车造型草图设计实验进行了测试,测试结果表明所提出的算法具有较高的运行效率,并能较好地克服用户疲劳问题.