蜕变测试和断言检查的比较与实验研究

在软件测试中,测试预言是一种用于检查程序在测试中是否正常运行的机制.然而在某些实际情况下,还无法制定测试预言或者难以有效地应用测试预言.针对此类测试预言问题,蜕变测试于近年应运而生,但蜕变测试的效率问题还没有被充分地加以研究.作者用控制实验的方法研究了使用蜕变测试的成本及效率,进而将蜕变测试和常用的断言检查两种方法的错误检测率和时间成本进行了比较和分析.实验结果表明,相比于断言检查方法,蜕变测试具有检测到更多错误的潜力.通过分析蜕变测试的效率和性能,与断言测试相比,蜕变测试的错误检测率更高效而效率有待提高,可适用于较为粗粒度的测试需求.     

蜕变测试研究进展及其在并行程序测试中的研究展望

在软件测试过程中,待测程序的预期输出是判断软件是否存在缺陷的重要因素.蜕变测试技术是利用被测软件的属性来检查程序输出,从而有效地解决程序预期输出难以构造的问题.近年来,蜕变测试在软件测试领域取得了蓬勃的发展,许多研究人员将蜕变测试技术进行优化,将其运用到各个领域,有效提高了软件质量.从原理、过程及其优化,应用领域3个方面,总结蜕变测试的研究工作,着重分析了近5年的研究进展,进一步展望了蜕变测试用于并行程序时,可能的研究主题.首先,介绍蜕变测试的基本概念和蜕变测试过程;接着,从蜕变关系、测试用例、测试执行过程以及蜕变测试工具4个角度,总结蜕变测试优化技术;然后,汇总了蜕变测试的应用领域;最后,基于已有研究成果,讨论蜕变测试在并行程序测试领域面临的问题,为蜕变技术在并行程序测试领域的研究提供可能的思路.     

一种基于蜕变关系的测试与失效测试用例定位模型

针对传统的蜕变测试模型MTM存在的局限性,提出了一种基于蜕变关系的测试模型MRTM。首先通过对比分析,指出了MRTM的适用范围等特点;其次,针对MTM和MRTM都面临的失效测试用例难以确定的难题,提出了一种基于可疑度计算的蜕变测试失效测试用例定位方法FTCL-MT。FTCL-MT作为对已有测试模型的补充,能够在蜕变关系不满足的情况下实现精确定位失效测试用例,从而能够为现有的故障定位技术提供支持。最后,通过实验验证了FTCL-MT方法的有效性。     

蜕变测试技术综述

软件测试是一种重要的、不可缺少的软件质量保证技术,用于发现和纠正软件中存在的缺陷和错误,但在很多情况下待测程序的预期输出难以确定。蜕变测试技术通过检查程序的多个执行结果之间的关系来测试程序,可以有效地解决上述问题。经过近十年的研究,蜕变测试技术已经在测试过程的优化、与其他验证或测试方法的结合等方面取得了巨大的进展,并被广泛地应用于各个领域中。对当前蜕变测试技术的研究进行了综述,针对已有方法的不足之处,对未来的研究方向进行了展望,包括蜕变测试充分性研究、实用蜕变关系构造技术、实用原始测试用例选取技术、新型软件中蜕变测试技术的研究、蜕变测试工具的开发等。     

蜕变测试技术综述

软件测试是一种重要的、不可缺少的软件质量保证技术,用于发现和纠正软件中存在的缺陷和错误,但在很多情况下待测程序的预期输出难以确定。蜕变测试技术通过检查程序的多个执行结果之间的关系来测试程序,可以有效地解决上述问题。经过近十年的研究,蜕变测试技术已经在测试过程的优化、与其他验证或测试方法的结合等方面取得了巨大的进展,并被广泛地应用于各个领域中。对当前蜕变测试技术的研究进行了综述,针对已有方法的不足之处,对未来的研究方向进行了展望,包括蜕变测试充分性研究、实用蜕变关系构造技术、实用原始测试用例选取技术、新型软件中蜕变测试技术的研究、蜕变测试工具的开发等。     

针对模型转换程序的蜕变测试方法研究

模型驱动架构中模型转换结果正确与否常常难以判断(即测试Oracle问题),而蜕变测试通过验证多个执行结果之间是否满足蜕变关系可以部分地解决测试Oracle问题。为有效地解决模型转换测试中的Oracle问题,以UML到Java模型转换程序为例,应用蜕变测试,依据转换规则,从增加、删除、修改、替换4个方面设计并构造出一组蜕变关系。最后对待测程序植入在实际测试中常见的两种错误,设计并执行测试用例后验证蜕变关系,发现违反了蜕变关系,暴露出程序缺陷,从而说明了蜕变测试的有效性。     

蜕变关系构造基本准则与策略研究

蜕变测试可以部分解决软件测试中的Oracle问题,其关键步骤和难点是蜕变关系的构造,它将直接影响测试的效果。通过对典型程序测试的案例对蜕变关系的构造进行分析,归纳总结了若干构造蜕变关系的基本准则,并在案例研究中采用变异分析方法验证了构造准则的合理性。提出了蜕变测试与等价类测试结合运用的测试方法,此方法可用于输入空间易于分类的程序。     

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。     

坡度坡向量算程序蜕变测试方法

坡度、坡向量算是地理信息系统的主要功能之一。然而,由于截断误差、舍入误差以及误差传播的影响,导致测试判定难以获取。基于此,提出将蜕变测试应用于坡度、坡向量算程序的测试中,通过分析坡度、坡向量算程序功能的几何属性、数值计算特性以及具体实现算法等提出蜕变关系,并通过分析蜕变关系的适用范围,形成坡度坡向量算程序蜕变测试方法。最后,通过实例研究,验证了提出的蜕变关系在消除不同类型变异方面的有效性。实验结果表明,该方法可有效解决坡度、坡向量算程序的测试判定问题,同时也为解决地理信息系统中其他空间度量程序的测试判定问题提供了借鉴,进一步拓展了蜕变测试技术的应用范围。     

面向条件筛选搜索系统的蜕变测试方法

为缓解拥有庞大数据信息的条件筛选搜索系统搜索结果时带来的Oracle问题,提出蜕变测试方法.通过识别程序多次输入输出之间的关系是否违反蜕变关系,可有效缓解Oracle问题.因此,有效识别蜕变关系是完成蜕变测试的前提.根据条件筛选搜索系统的自定义条件,设计两种蜕变关系模式帮助简化蜕变关系的识别,提高蜕变测试的故障检测质量.实验结果表明,利用改进的蜕变关系模式在条件筛选搜索系统中执行蜕变测试提高了14％的精确率,验证了该方法识别蜕变关系的简洁性和有效性.     

基于Python的机器视觉实验教学平台设计

针对机器视觉领域的学习内容抽象、难以理解,相关的实验教学产品不足这些问题,基于Python语言,采用开源的opencv-python图像处理库与TensorFlow机器学习框架,提出构建机器视觉实验教学平台。该系统涵盖机器视觉的经典方法,主要包括向量机、K临近图像分类,神经网络、卷积神经网络目标识别,基于经典方法融合常用函数,对系统分模块设计。经过测试,该系统具有较好的交互性与可扩展性,可以适应机器视觉的实验要求,训练数据、样本测试数据导入灵活,机器视觉参数优化、代码迭代方便,并且能够编译生成.exe可执行文件,辅助学生学习机器视觉技术的真实应用场景,提高学生实践解决问题能力和创新能力。     

基于SVM的离线图像目标分类算法

目标分类是计算机视觉与模式识别领域的关键环节. SVM(支持向量机)是在统计学习理论基础上提出的一种新的机器学习方法.提出一种支持向量机结合梯度直方图特征的离线图像目标分类算法.首先对训练集进行预处理,然后对处理后的图片进行梯度直方图特征提取,最后通过训练得到可以检测图像目标的分类器.利用得到的分类器对测试图片进行测试,测试结果表明,对目标分类检测有良好的效果.     

Predicting metamorphic relations for testing scientific software: a machine learning approach using graph kernels

Comprehensive, automated software testing requires an oracle to check whether the output produced by a test case matches the expected behaviour of the programme. But the challenges in creating suitable oracles limit the ability to perform automated testing in some programmes, and especially in scientific software. Metamorphic testing is a method for automating the testing process for programmes without test oracles. This technique operates by checking whether the programme behaves according to properties called metamorphic relations. A metamorphic relation describes the change in output when the input is changed in a prescribed way. Unfortunately, finding the metamorphic relations satisfied by a programme or function remains a labour‐intensive task, which is generally performed by a domain expert or a programmer. In this work, we propose a machine learning approach for predicting metamorphic relations that uses a graph‐based representation of a programme to represent control flow and data dependency information. In earlier work, we found that simple features derived from such graphs provide good performance. An analysis of the features used in this earlier work led us to explore the effectiveness of several representations of those graphs using the machine learning framework of graph kernels, which provide various ways of measuring similarity between graphs. Our results show that a graph kernel that evaluates the contribution of all paths in the graph has the best accuracy and that control flow information is more useful than data dependency information. The data used in this study are available for download at http://www.cs.colostate.edu/saxs/MRpred/functions.tar.gz to help researchers in further development of metamorphic relation prediction methods. Copyright © 2015 John Wiley & Sons, Ltd.     

Overlapping area hyperspheres for kernel-based similarity method

Measuring similarity between sets of objects is a key step in a wide areas of machine learning. Popular examples include general classification framework and numerous applications in computer vision. In this paper, we propose a kernel-based similarity method which is inspired from an interesting biological behavior of trees and induced mathematically by formulating it as a quadratic optimization problem in a reproducing kernel Hilbert space (RKHS). The proposed method is compared to the maximum mean discrepancy, a recent and challenging kernel similarity method. We conduct and present several numerical experiments on synthetic data as well as real-word image data. The proposed method yields favorable performances in terms of classification performances in the context of supervised classification tasks on the challenging Caltech101 dataset and other datasets such as USPS and ETH80. Furthermore, the efficiency of the proposed method in the context of image segmentation through unsupervised clustering of superpixels has been also asserted.     

计算机视觉下的车辆目标检测算法综述

车辆目标检测是基于计算机视觉的目标检测领域的一个重要应用领域,近年来随着深度学习在图像分类方面取得的巨大进展,机器视觉技术结合深度学习方法的车辆目标检测算法逐渐成为该领域的研究重点和热点。介绍了基于机器视觉的车辆目标检测的任务、难点与发展现状,以及深度学习方法中几种具有代表性的卷积神经网络模型,通过这些网络模型衍生出的two stage、one stage车辆目标检测算法和用于模型训练的相关数据集与检测效果评价标准,对其存在的问题及未来可能的发展方向进行了讨论。     

Deep topology network: A framework based on feedback adjustment learning rate for image classification

Convolutional Neural Network (CNN) has demonstrated its superior ability to achieve amazing accuracy in computer vision field. However, due to the limitation of network depth and computational complexity, it is still difficult to obtain the best classification results for the specific image classification tasks. In order to improve classification performance without increasing network depth, a new Deep Topology Network (DTN) framework is proposed. The key idea of DTN is based on the iteration of multiple learning rate feedback. The framework consists of multiple sub-networks and each sub-network has its own learning rate. After the determined iteration period, these learning rates can be adjusted according to the feedback of training accuracy, in the feature learning process, the optimal learning rate is updated iteratively to optimize the loss function. In practice, the proposed DTN framework is applied to several state-of-the-art deep networks, and its performance is tested by extensive experiments and comprehensive evaluations of CIFAR-10 and MNIST benchmarks. Experimental results show that most deep networks can benefit from the DTN framework with an accuracy of 99.5% on MINIST dataset, which is 5.9% higher than that on the CIFAR-10 benchmark.     

Common-sense reasoning for human action recognition

This paper presents a novel method that leverages reasoning capabilities in a computer vision system dedicated to human action recognition. The proposed methodology is decomposed into two stages. First, a machine learning based algorithm – known as bag of words – gives a first estimate of action classification from video sequences, by performing an image feature analysis. Those results are afterward passed to a common-sense reasoning system, which analyses, selects and corrects the initial estimation yielded by the machine learning algorithm. This second stage resorts to the knowledge implicit in the rationality that motivates human behaviour. Experiments are performed in realistic conditions, where poor recognition rates by the machine learning techniques are significantly improved by the second stage in which common-sense knowledge and reasoning capabilities have been leveraged. This demonstrates the value of integrating common-sense capabilities into a computer vision pipeline.     

A Survey of Image Synthesis Methods for Visual Machine Learning

Image synthesis designed for machine learning applications provides the means to efficiently generate large quantities of training data while controlling the generation process to provide the best distribution and content variety. With the demands of deep learning applications, synthetic data have the potential of becoming a vital component in the training pipeline. Over the last decade, a wide variety of training data generation methods has been demonstrated. The potential of future development calls to bring these together for comparison and categorization. This survey provides a comprehensive list of the existing image synthesis methods for visual machine learning. These are categorized in the context of image generation, using a taxonomy based on modelling and rendering, while a classification is also made concerning the computer vision applications they are used. We focus on the computer graphics aspects of the methods, to promote future image generation for machine learning. Finally, each method is assessed in terms of quality and reported performance, providing a hint on its expected learning potential. The report serves as a comprehensive reference, targeting both groups of the applications and data development sides. A list of all methods and papers reviewed herein can be found at https://computergraphics.on.liu.se/image_synthesis_methods_for_visual_machine_learning/ .