软件测试设计重要度的决策分析

针对软件测试设计策略优化问题,提出一种基于测试重要度的测试设计决策分析算法。该方法依据测试有效性原则,采用层次分析理论,通过定量和定性相结合的方法,解决软件测试过程中测试重要度比较和分析问题,完成对软件测试设计工作的重点或优先级的决策分析,优化测试设计策略,进而合理地分配测试资源,有效发现软件存在的缺陷,提高软件测试的效率。     

Real options analysis of the timing of IS investment decisions

Many information systems (IS) investments belong to a class of capital budgeting problem where there is an option: the investment may be made straight away or delayed for some period. A real options analysis could allow decision-makers to add value to these investment decisions by providing a framework that explicitly recognises uncertainty. This paper uses options pricing theory to determine the optimal timing of IS investments and to explore the effect of different investment review cycles. The findings provide support for the common industry practice of demanding short payback periods for IS investments.     

The impact of decision support system features on user overconfidence and risky behavior

There is considerable research on how Decision Support Systems (DSSs) enable users to make better decisions. However, there is less focus on the possibility that some of their features may introduce biases and encourage suboptimal user behavior. We report on an experimental study that examines three DSS features that are generally considered beneficial to the user: the degree of choice the system provides to the user, the presence of competition among concurrent users, and the use of training to increase system familiarity. We hypothesize that the three DSS features may increase risky behavior (measured as the amount invested in a stock investment task with random outcomes) and overconfidence, conceptualized as the illusion of control, the phenomenon whereby people believe their chances of success at a task are greater than would be warranted by objective analysis. Our results confirm the effects of the three DSS features on risky behavior but only degree of choice impacts overconfidence. Moreover, overconfidence does not appear to mediate the impact of the DSS features on risky behavior. Finally, we hypothesize and confirm that, controlling for the effect of actual performance, overconfidence increases user satisfaction with the decision-making process and outcome.     

考虑准则依赖的风险型多准则决策新方法

为有效应对风险型多准则决策过程中准则依赖的问题,针对现有文献中完全理性和有限理性决策两种视角下准则独立假设的不足,应用DEMATEL方法分析决策准则之间的相互影响关系,得到准则依赖情境下的综合重要性权重,进而以备选方案状态均值为参照点,从前景理论出发,求解风险状态下备选方案的综合前景价值,作为决策依据。案例应用结果表明,所提方法在实践中是切实可行的。     

Measuring the intangibles in social decisions: Assessing benefits and costs of energy policy options

This article describes the use of the Analytic Hierarchy Process (AHP) of Saaty for measuring intangibles in social decision-making. The traditional cost-benefit analysis approach is discussed and some key problems pointed out. Use of AHP to avoid some of these problems is discussed. An example is given of applying AHP to assess social, political, economic and environmental benefits and costs of energy policy options.     

Statistical shape analysis: clustering, learning, and testing

Using a differential-geometric treatment of planar shapes, we present tools for: 1) hierarchical clustering of imaged objects according to the shapes of their boundaries, 2) learning of probability models for clusters of shapes, and 3) testing of newly observed shapes under competing probability models. Clustering at any level of hierarchy is performed using a minimum variance type criterion and a Markov process. Statistical means of clusters provide shapes to be clustered at the next higher level, thus building a hierarchy of shapes. Using finite-dimensional approximations of spaces tangent to the shape space at sample means, we (implicitly) impose probability models on the shape space, and results are illustrated via random sampling and classification (hypothesis testing). Together, hierarchical clustering and hypothesis testing provide an efficient framework for shape retrieval. Examples are presented using shapes and images from ETH, Surrey, and AMCOM databases.     

Risk-taking on the road and in the mind: behavioural and neural patterns of decision making between risky and safe drivers

Drivers' risk-taking is a key issue of road safety. This study explored individual differences in drivers' decision-making, linking external behaviours to internal neural activity, to reveal the cognitive mechanisms of risky driving. Twenty-four male drivers were split into two groups (risky vs. safe drivers) via the Drivier Behaviour Questionnaire-violation. The risky drivers demonstrated higher preference for the risky choices in the paradigms of Iowa Gambling Task and Balloon Analogue Risk Task. More importantly, the risky drivers showed lower amplitudes of feedback-related negativity (FRN) and loss-minus-gain FRN in both paradigms, which indicated their neural processing of error-detection. A significant difference of P300 amplitudes was also reported between groups, which indicated their neural processing of reward-evaluation and were modified by specific paradigm and feedback. These results suggested that the neural basis of risky driving was the decision patterns less revised by losses and more motivated by rewards.     

Numerical analysis of continuous time Markov decision processes over finite horizons

Continuous time Markov decision processes (CTMDPs) with a finite state and action space have been considered for a long time. It is known that under fairly general conditions the reward gained over a finite horizon can be maximized by a so-called piecewise constant policy which changes only finitely often in a finite interval. Although this result is available for more than 30 years, numerical analysis approaches to compute the optimal policy and reward are restricted to discretization methods which are known to converge to the true solution if the discretization step goes to zero. In this paper, we present a new method that is based on uniformization of the CTMDP and allows one to compute an ε-optimal$\epsilon \text{-optimal}$ policy up to a predefined precision in a numerically stable way using adaptive time steps.     

Mathematical modeling of decision making: a soft and fuzzy approach to capturing hard decisions

This research focuses on a modeling approach and set of mathematical tools that were derived from research on intelligence systems, namely fuzzy system modeling. This study systematically evaluates these tools as an approach for modeling human decision making, contrasting the approach with more traditional methods based on regression. The research was conducted using experts and a simulated task environment related to allocating rewards in the form of merit pay. The results indicate that fuzzy system models generally perform as well as or better than both linear and nonlinear regression methods in terms of model fit. These results are discussed in terms of issues regarding modeling precision versus parsimony, the value of adaptive modeling techniques, empirical versus subjective approaches to model building, and individual differences in judgment strategies. Potential applications of this research include using the modeling approach studied to build higher-fidelity models that yield new insights and a better understanding of decision-making strategies and environments.     

RETRACTED ARTICLE: Comparative analysis of time series model and machine testing systems for crime forecasting

Crime forecasting has been one of the most complex challenges in law enforcement today, especially when an analysis tends to evaluate inferable and expanded crime rates, although a few methodologies for subsequent equivalents have been embraced before. In this work, we use a strategy for a time series model and machine testing systems for crime estimation. The paper centers on determining the quantity of crimes. Considering various experimental analyses, this investigation additionally features results obtained from a neural system that could be a significant alternative to machine learning and ordinary stochastic techniques. In this paper, we applied various techniques to forecast the number of possible crimes in the next 5 years. First, we used the existing machine learning techniques to predict the number of crimes. Second, we proposed two approaches, a modified autoregressive integrated moving average model and a modified artificial neural network model. The prime objective of this work is to compare the applicability of a univariate time series model against that of a variate time series model for crime forecasting. More than two million datasets are trained and tested. After rigorous experimental results and analysis are generated, the paper concludes that using a variate time series model yields better forecasting results than the predicted values from existing techniques. These results show that the proposed method outperforms existing methods.

     

Value of agreement in decision analysis: Concept,measures and application

In multi-criteria decision analysis workshops, participants often appraise the options individually before discussing the scoring as a group. The individual appraisals lead to score ranges within which the group then seeks the necessary agreement to identify their preferred option. Preference programming enables some options to be identified as dominated even before the group agrees on a precise scoring for them.Workshop participants usually face time pressure to make a decision. Decision support can be provided by flagging options for which further agreement on their scores seems particularly valuable. By valuable, we mean the opportunity to identify other options as dominated (using preference programming) without having their precise scores agreed beforehand. The present paper quantifies this Value of Agreement and extends the concept to portfolio decision analysis and criterion weights. The new concept is validated through a case study in recruitment.     

Classification of bioacoustic time series based on the combination of global and local decisions

Automated classification of cricket songs from Thailand and Ecuador is the topic of this study. For this, the locations of pulses are determined and different features in the time and the frequency domain are extracted automatically from the time series. For the categorization of the sound patterns these features are combined through data fusion, temporal fusion and decision fusion. Local features and global features are distinguished. For the classification a fuzzy-k-nearest-neighbour classifier was used. Classification results for a data set containing songs of 28 different species are presented.     

In Google We Trust: Users' Decisions on Rank, Position, and Relevance

An eye tracking experiment revealed that college student users have substantial trust in Google's ability to rank results by their true relevance to the query. When the participants selected a link to follow from Google's result pages, their decisions were strongly biased towards links higher in position even if the abstracts themselves were less relevant. While the participants reacted to artificially reduced retrieval quality by greater scrutiny, they failed to achieve the same success rate. This demonstrated trust in Google has implications for the search engine's tremendous potential influence on culture, society, and user traffic on the Web.     

更改的判定条件覆盖测试技术研究

与语句覆盖、判定覆盖、条件覆盖、判定条件覆盖、多重条件覆盖相比，路径覆盖具有更强的结构覆盖率。但其需要更庞大的测试用例，事实上它并不实用。近年来，一种相对新颖和优秀的结构覆盖准则一一更改的判定条件覆盖(MC/DC)应运而生，它继承了多重条件覆盖的优点，同时只是线性地增加了测试用例的数量。因此，在许多重要的软件，尤其是以嵌入式和实时性为特征的航空机载软件中得到广泛的应用．介绍了MC／DC的特点、设计方法、评估策略及工程应用。     

Reputation,framing strategies and user's choice of content on the Web: an empirical study

Current trends on the Internet indicate an increasing supply of content from anonymous users (e.g. blogs), which may become popular among website visitors. Motivated by these Internet trends, the present study explores the tradeoffs between the source's reputation and the way content is displayed or offered on the web page as well as the effects of both on the Internet user's choice of information services. The study builds upon behavioral economics and focuses on the interaction between background and local context effects on the individual's choice. In particular, the study investigates the tradeoffs between the source's reputation, representing the background context and the provision of enriched content (e.g. source bundled with a picture or a video) representing local context. The research involved a set of experiments based on hypothetical choice or matching tasks. The main findings indicate that there are significant interaction effects between reputation and enriched content, in the form of content related pictures, video and users' recommendations, which affect the user's choice of information services. Copyright © 2009 John Wiley & Sons, Ltd.     

基于连续时间MDP模型和随机决策的维护周期

生产系统的维护策略直接关系到设备的使用寿命,对生产线的连续性和可靠性,以及产品质量、生产效率、满足率等方面都有影响。利用连续时间的MDP模型研究单台设备的维护策略,综合考虑转移概率的动态性和方案选择的随机性,利用MATLAB软件实现优化,获得最佳维护周期。将其应用于混联结构,与基于离散空间的MDP维护策略进行比较。结果显示,基于连续时间MDP模型的维护策略能够有效提高生产系统性能,降低成本,提高生产效益。     

基于遗传算法和模糊决策树的时间序列预测模型

股票价格预测总是投资者和技术分析者感兴趣的一个主题.然而,决定买卖股票的最好时间仍然是困难的,因为有很多因素可能影响股票价格.通过改进模糊决策树建立了一个新型金融时间序列数据预测模型.该预测模型融合数据聚类技术,模糊决策树及遗传算法来构建基于历史数据和技术指标的一个决策系统.提出的GAFDT模型在与各种股票的其它方法相比较时有平均预测准确率为0.82的最好绩效.