Linking software testing results with a machine learning approach

Software testing techniques and criteria are considered complementary since they can reveal different kinds of faults and test distinct aspects of the program. The functional criteria, such as Category Partition, are difficult to be automated and are usually manually applied. Structural and fault-based criteria generally provide measures to evaluate test sets. The existing supporting tools produce a lot of information including: input and produced output, structural coverage, mutation score, faults revealed, etc. However, such information is not linked to functional aspects of the software. In this work, we present an approach based on machine learning techniques to link test results from the application of different testing techniques. The approach groups test data into similar functional clusters. After this, according to the tester's goals, it generates classifiers (rules) that have different uses, including selection and prioritization of test cases. The paper also presents results from experimental evaluations and illustrates such uses.     

支持向量机的进化多核设计

为提高支持向量机分类精度,提出一种基于遗传程序设计的进化多核算法.算法中每个个体表示一个多核函数,并采用树形结构进行编码,增强了多核函数的非线性;初始种群由生长法产生,经过遗传操作后得到适合具体问题的进化多核函数.遗传程序设计的全局搜索性能使得算法设计不需要先验知识.与单核函数及其他多核函数的对比实验结果表明,进化多核...     

面向服务软件的蜕变测试方法

在面向服务软件的测试过程中,由于在服务发现之前不可知的交互对象和同一个服务可能会有不同实现,往往出现程序执行结果不能提前预知的Oracle问题。为了有效地解决面向服务软件测试中的Oracle问题,基于面向服务架构(SOA)的特点,提出将蜕变测试方法用于面向服务软件的单元测试和集成测试过程中,依据面向服务软件每个服务的自身性质构造蜕变关系,设计蜕变测试类执行测试用例并验证蜕变关系是否保持,如果蜕变关系被违反了,则发现和报告缺陷,从而有效地支持面向服务软件的测试。     

一种基于有向图的工作流测试方法

已有工作流测试方法在测试完备性和充分性方面存在不足。针对此问题文章对有向图工作流模型进行扩展,提出了一种新的工作流测试方法,设计并实现了其算法。该方法能够自动生成完备且充分的测试路径和测试用例。实例验证该方法有效。     

Virtual Battery: A testing tool for power-aware software

Virtualization is an inexpensive and convenient method for setting up software test environments. Thus it is being widely used as a test tool for software products requiring high reliability such as mission critical cyber-physical systems. However, existing virtualization platforms do not fully virtualize the battery subsystem. Therefore, it is difficult to test battery-related features of guest systems. In this paper, we propose Virtual Battery, a battery virtualization scheme for type II full virtualization platforms. Virtual Battery takes the form of an ACPI-compatible battery device driver dedicated to each virtual machine, which virtualizes a target system. Through Virtual Battery, developers can easily manipulate the charging and battery status of each virtual machine (VM), regardless of the existence or current status of the host system’s battery. In addition, Virtual Battery emulates the behavior of batteries by discharging the virtual batteries according to the resource usages of their VMs. This feature enables VMs to act as battery resource containers. Three case studies demonstrate the effectiveness of the proposed scheme.     

图像多分类主动学习方法

以决策速度快的决策导向非循环图支持向量机（Decision Directed Acyclic Graph Support Vector Machine）为基准分类器,结合主动学习的思想,提出了一种图像多分类主动学习方法。这种方法是一种半自动的图像语义分类方法,可以将图像分成多个语义类别。该方法在最近边界主动选择方法的基础上,提出一种基于质疑度的主动选择策略。这种策略将SVMactive中提出的最近邻SVM分类面选择的反馈样例策略延伸到多分类中,通过区别对待奇异样例和容易错分样例,减少了噪声数据对分类器的干扰,提高了分类的精度。     

Support vector machine classification with noisy data: a second order cone programming approach

In this paper, we investigate the stability of linear and quadratic programming support vector machines (SVMs) with bounded noise in the input data using a robust optimisation model. For a linear discriminant function, this model is expressed as a second order cone optimisation problem. Using the concept of the kernel function, we generalise for nonlinear discriminant functions. Intuitively, it looks quite clear that large margin classifiers are robust in terms of bounded input noise. However, there is no theoretical analysis investigating this behaviour. We show that the SVM solution is stable under bounded perturbations of the data both in the linear programming and quadratic programming. Computational results are also presented for toy and real-world data.     

String kernels construction and fusion: a survey with bioinformatics application

The kernel method, especially the kernel-fusion method, is widely used in social networks, computer vision, bioinformatics, and other applications. It deals effectively with nonlinear classification problems, which can map linearly inseparable biological sequence data from low to high-dimensional space for more accurate differentiation, enabling the use of kernel methods to predict the structure and function of sequences. Therefore, the kernel method is significant in the solution of bioinformatics problems. Various kernels applied in bioinformatics are explained clearly, which can help readers to select proper kernels to distinguish tasks. Mass biological sequence data occur in practical applications. Research of the use of machine learning methods to obtain knowledge, and how to explore the structure and function of biological methods for theoretical prediction, have always been emphasized in bioinformatics. The kernel method has gradually become an important learning algorithm that is widely used in gene expression and biological sequence prediction. This review focuses on the requirements of classification tasks of biological sequence data. It studies kernel methods and optimization algorithms, including methods of constructing kernel matrices based on the characteristics of biological sequences and kernel fusion methods existing in a multiple kernel learning framework.     

Classification of spectra of emission line stars using machine learning techniques

Advances in the technology of astronomical spectra acquisition have resulted in an enormous amount of data available in world-wide telescope archives. It is no longer feasible to analyze them using classical approaches, so a new astronomical discipline,astroinformatics, has emerged. We describe the initial experiments in the investigation of spectral line profiles of emission line stars using machine learning with attempt to automatically identify Be and B[e] stars spectra in large archives and classify their types in an automatic manner. Due to the size of spectra collections, the dimension reduction techniques based on wavelet transformation are studied as well. The result clearly justifies that machine learning is able to distinguish different shapes of line profiles even after drastic dimension reduction.     

Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques

The aim of the research is evaluating the classification performances of eight different machine-learning methods on the antepartum cardiotocography (CTG) data. The classification is necessary to predict newborn health, especially for the critical cases. Cardiotocography is used for assisting the obstetricians’ to obtain detailed information during the pregnancy as a technique of measuring fetal well-being, essentially in pregnant women having potential complications. The obstetricians describe CTG shortly as a continuous electronic record of the baby's heart rate took from the mother's abdomen. The acquired information is necessary to visualize unhealthiness of the embryo and gives an opportunity for early intervention prior to happening a permanent impairment to the embryo. The aim of the machine learning methods is by using attributes of data obtained from the uterine contraction (UC) and fetal heart rate (FHR) signals to classify as pathological or normal. The dataset contains 1831 instances with 21 attributes, examined by applying the methods. In the paper, the highest accuracy displayed as 99.2%.     

Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation

Due to the economic significance of bankruptcy prediction of companies for financial institutions, investors and governments, many quantitative methods have been used to develop effective prediction models. Support vector machine (SVM), a powerful classification method, has been used for this task; however, the performance of SVM is sensitive to model form, parameter setting and features selection. In this study, a new approach based on direct search and features ranking technology is proposed to optimise features selection and parameter setting for 1-norm and least-squares SVM models for bankruptcy prediction. This approach is also compared to the SVM models with parameter optimisation and features selection by the popular genetic algorithm technique. The experimental results on a data set with 2010 instances show that the proposed models are good alternatives for bankruptcy prediction.     

Specification and testing using generalized machines: a presentation and a case study

Although testing is a major part of software development, it rarely gets the attention it deserves from researchers, partly because its foundations are weak and ill-understood. The principal purpose of testing is to detect (and then remove) faults in a software system. However, very few of the existing methods allow the tester to make any precise statement about the type or number of faults that remain undetected after testing is completed. In particular, none of the main techniques used by the software industry can give serious guarantees that a system is fault-free after testing has been completed. This paper advocates the use of a formal method both as a specification language and as the basis of a test data selection strategy. It presents a new method for generating test cases from this type of formal specification that provides a more convincing answer to the problem of detecting all faults in a software system. The method is reductionist in the sense that it guarantees that a system is fault-free provided that its components are fault-free; in turn, the same method could be used to test the resulting sub-systems, so the reduction will continue until the components considered are either known to be correct or are fairly simple pieces of code that can be successfully tested using traditional methods. The formal method used, X-machines, is a blend of finite state machines, data structures and processing functions and provides a simple and intuitive way of specifying computer systems. The use of X-machines as a specification tool and the testing method are illustrated with a case study. © 1998 John Wiley & Sons, Ltd.     

Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach

The effective recognition of unnatural control chart patterns (CCPs) is a critical issue in statistical process control, as unnatural CCPs can be associated with specific assignable causes adversely affecting the process. Machine learning techniques, such as artificial neural networks (ANNs), have been widely used in the research field of CCP recognition. However, ANN approaches can easily overfit the training data, producing models that can suffer from the difficulty of generalization. This causes a pattern misclassification problem when the training examples contain a high level of background noise (common cause variation). Support vector machines (SVMs) embody the structural risk minimization, which has been shown to be superior to the traditional empirical risk minimization principle employed by ANNs. This research presents a SVM-based CCP recognition model for the on-line real-time recognition of seven typical types of unnatural CCP, assuming that the process observations are AR(1) correlated over time. Empirical comparisons indicate that the proposed SVM-based model achieves better performance in both recognition accuracy and recognition speed than the model based on a learning vector quantization network. Furthermore, the proposed model is more robust toward background noise in the process data than the model based on a back propagation network. These results show the great potential of SVM methods for on-line CCP recognition.     

InRob: An approach for testing interoperability and robustness of real-time embedded software

Advances in digital technologies have contributed for significant reduction in accidents caused by hardware failures. However, the growing complexity of functions performed by embedded software has increased the number of accidents caused by software faults in critical systems. Moreover, due to the highly competitive market, software intensive subsystems are usually developed by different suppliers. Often these subsystems are required to interact with each other in order to provide a collaborative service. Testing approaches for subsystems integration support verification of the quality of service, focusing on the subsystems interfaces. The increasing complexity and tight coupling of real-time subsystems make integration testing unmanageable. The ad-hoc approach for testing is becoming less effective and more expensive. This article presents an integration testing approach denominated InRob, designed to verify the interoperability and robustness related to timing constraints of real-time embedded software. InRob guides the construction of services, based on formal models, aiming at the specifications of interoperability and robustness of test cases related to delays and time-outs of the messages exchanged in the interfaces of interconnected subsystems. The proposed formalism supports automatic test cases generation by verifying the relevant properties in the service behavioral model. As timing constraints are critical properties of aerospace systems, the feasibility of InRob is showed in the integration testing process of a telescope onboard in a satellite. The process is instantiated with existing testing tools and the case study is the software embedded in the telescope.     

On the time series support vector machine using dynamic time warping kernel for brain activity classification

A new data mining technique used to classify normal and pre-seizure electroencephalograms is proposed. The technique is based on a dynamic time warping kernel combined with support vector machines (SVMs). The experimental results show that the technique is superior to the standard SVM and improves the brain activity classification. This research was partially supported by Rutgers Research Council grant 202018, NSF grants CCF-0546574, DBI-980821, EIA-9872509, and CCF 0546574, and NIH grant R01-NS-39687-01A1. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 159–173, January–February 2008.     

Generic methodology for the probabilistic reliability assessment of some structural properties: a graph theoretical approach

The graphical characterisation of many important structural properties, such as controllability, observability, diagnosability of many kinds of structured systems, uses mainly four types of elementary graphical conditions: connectivity, complete matching, linking and distance conditions. Since structural properties depend on different associations of elementary conditions, it is interesting to study elementary conditions. This paper is the first part of this global approach based on elementary graphical conditions and we choose to study the so-called connectivity and complete matching conditions. Starting from the graphical representation associated with a system, the paper provides Boolean expressions of the connectivity and complete matching conditions based on the edges validity, which can be linked to the physical components operating state. These expressions can then be used to define and compute the reliability of a structural property knowing the reliability of the system physical components. This knowledge can be important during both conception and exploitation stages. The proposed methods are quite intuitive and simple to implement and have basically polynomial complexity orders. This makes our approach well suited to analyse large-scale systems.     

Phish-Sight: a new approach for phishing detection using dominant colors on web pages and machine learning

International Journal of Information Security - Phishing is one of the most dangerous threats in which a hacker imitates a person, company or government agency to lure and deceive their victims....     

Parametric models and non-parametric machine learning models for predicting option prices: Empirical comparison study over KOSPI 200 Index options

We investigated the performance of parametric and non-parametric methods concerning the in-sample pricing and out-of-sample prediction performances of index options. Comparisons were performed on the KOSPI 200 Index options from January 2001 to December 2010. To verify the statistical differences between the compared methods, we tested the following null hypothesis: two series of forecasting errors have the same mean-squared value. The experimental study reveals that non-parametric methods significantly outperform parametric methods on both in-sample pricing and out-of-sample pricing. The outperforming non-parametric method is statistically different from the other models, and significantly different from the parametric models. The Gaussian process model delivers the most outstanding performance in forecasting, and also provides the predictive distribution of option prices.