Complete model-based equivalence class testing

In this article, we present a model-based black-box equivalence partition testing strategy, together with a formal proof of its completeness properties. The results apply to reactive systems with large, possibly infinite input data types and finite internal and output data ranges that may be enumerated with acceptable effort. The investigation is performed on a semantic level and applies to all concrete test models whose behavioural semantics can be encoded as a variant of state transition systems. Test suite construction is performed in relation to a given fault model \(\mathcal{F}\) for which a finite black-box test suite can be constructed which is complete with respect to \(\mathcal{F}\). It is shown how the test suite generation can be effectively implemented by model-based testing tools, using propositional representations of behavioural model semantics and constraint solvers. A SysML model of the ceiling speed monitoring function of the European Train Control System is presented as a case study, to explain theory application to a concrete modelling formalism.     

Component architecture of model-based testing environment

In the paper, an approach to constructing architecture of tools for model-based testing that rely on modern component-based technologies is presented. One of the basic ideas underlying this approach consists in application of noninvasive composition techniques, which make it possible to integrate a set of independently developed components into a complex system and reconfigure it without modifying codes of the components. The approach suggested in the paper is one of the first applications of the component-based technologies to designing test systems. A prototype implementation of the suggested approach based on available libraries is described, and an example of its use for test construction is presented.     

Voltage driven hard disk drive with voice coil model-based control

In this paper, we describe a new solution for driving the voice coil motor (VCM) in hard disk drives (HDDs), in which the usual current driver for the voice coil motor has been replaced by a voltage driver. When used in conjunction with PWM power stages, this solution becomes fully digital and has proven advantages in terms of cost, power dissipation and silicon area. To achieve the same performance of the current driver, the voltage driver requires a pre-filter placed at its input, to cancel out the electrical pole of the VCM. In the paper, we describe the voice coil model-based control (VCMC), a solution for implementing the pre-filter, which replicates the behavior of a current loop by using a model of the voice coil motor and its driver, including the saturation of the power stage. It is worth noticing that VCM resistance may change by 30% during HDD operations, increasing when the disk is performing repetitive seeks or decreasing when the disk cools down during track following. Such variation may lead to an unsatisfactory performance of the VCMC, so an adaptation mechanism, capable of tracking variations of VCM coil resistance, must be set up. This paper presents a pair of on-line estimation procedure, used to get the value of the VCM coil resistance during seek and track following, respectively. The first one is based on a standard LMS approach, aimed at identifying a simplified model of the plant with the least computational effort. The second exploits the presence of repetitive disturbances (RROs) to detect variations of plant gain and, in turn, of VCM resistance. It will be shown that both procedures are quite accurate in estimating variations of VCM resistance and their output can be used to perform on-line tuning of the VCMC. Experimental results show that the servo performance with the adaptive VCMC is not affected by resistance variation and equivalent to that of the standard current driver.     

A novel model-based testing approach for software product lines

Model-based testing relies on a model of the system under test. FineFit is a framework for model-based testing of Java programs. In the FineFit approach, the model is expressed by a set of tables based on Parnas tables. A software product line is a family of programs (the products) with well-defined commonalities and variabilities that are developed by (re)using common artifacts. In this paper, we address the issue of using the FineFit approach to support the development of correct software product lines. We specify a software product line as a specification product line where each product is a FineFit specification of the corresponding software product. The main challenge is to concisely specify the software product line while retaining the readability of the specification of a single system. To address this, we used delta-oriented programming, a recently proposed flexible approach for implementing software product lines, and developed: (1) delta tables as a means to apply the delta-oriented programming idea to the specification of software product lines; and (2) DeltaFineFit as a novel model-based testing approach for software product lines.     

Wodel-Test: a model-based framework for language-independent mutation testing

Software and Systems Modeling - Mutation testing (MT) targets the assessment of test cases by measuring their efficiency to detect faults. This technique involves modifying the program under test...     

基于QTP的数据驱动测试开发

提出采用面向自动化测试的测试用例设计格式,通过QTP的编程功能,使用外部数据源来实现较复杂的数据驱动测试。具体方法是采用Excel作为测试数据源,制定Excel中测试数据的设计格式,建立专用的函数库来操作Excel数据,实现以数据为驱动的自动化测试。这一方法能有效地实现测试数据与测试脚本的分离,同时,测试数据的设计操作变得方便,数据的可读性增强,提高了测试效率和测试数据的维护效率。     

Experiences using Tedeso: an extensible and interoperable model-based testing platform

The integration of novel software quality assurance tools into existing development environments must be performed in ways that leverage the benefits of the tools while minimizing their impact on existing software processes. This supports the adoption of new methodologies with minimal interference into core business practices. This paper discusses the design of Tedeso, an extensible and interoperable model-based testing platform developed to facilitate the automatic generation of tests, while supporting the needs of different stakeholders in a diverse and broad organization. We discuss Tedeso key design characteristics, in particular its extensibility and interoperability, provided through the use of a workflow-driven service-oriented architecture, and show how it has enabled and facilitated the adoption of model-based testing techniques in different business units in different sectors within SIEMENS. We also discuss some issues that come from the adoption of service-oriented architectures, showing how they have been managed in our platform.     

Test case prioritization techniques for model-based testing: a replicated study

Recently, several test case prioritization (TCP) techniques have been proposed to order test cases for achieving a goal during test execution, particularly, revealing faults sooner. In the model-based testing (MBT) context, such techniques are usually based on heuristics related to structural elements of the model and derived test cases. In this sense, techniques’ performance may vary due to a number of factors. While empirical studies comparing the performance of TCP techniques have already been presented in literature, there is still little knowledge, particularly in the MBT context, about which factors may influence the outcomes suggested by a TCP technique. In a previous family of empirical studies focusing on labeled transition systems, we identified that the model layout, i.e., amount of branches, joins, and loops in the model, alone may have little influence on the effectiveness of TCP techniques investigated, whereas characteristics of test cases that actually fail definitely influences this aspect. However, we considered only synthetic artifacts in the study, which reduced the ability of representing properly the reality. In this paper, we present a replication of one of these studies, now with a larger and more representative selection of techniques and considering test suites from industrial systems as experimental objects. Our objective is to find out whether the results remain while increasing the validity in comparison to the original study. Results reinforce that there is no best performer among the investigated techniques and characteristics of test cases that fail represent an important factor, although adaptive random-based techniques are less affected by it.     

Sequential model-based segmentation and recognition of image structures driven by visual features and spatial relations

A sequential segmentation framework, where objects in an image are successively segmented, generally raises some questions about the “best” segmentation sequence to follow and/or how to avoid error propagation. In this work, we propose original approaches to answer these questions in the case where the objects to segment are represented by a model describing the spatial relations between objects. The process is guided by a criterion derived from visual attention, and more precisely from a saliency map, along with some spatial information to focus the attention. This criterion is used to optimize the segmentation sequence. Spatial knowledge is also used to ensure the consistency of the results and to allow backtracking on the segmentation order if needed. The proposed approach was applied for the segmentation of internal brain structures in magnetic resonance images. The results show the relevance of the optimization criteria and the interest of the backtracking procedure to guarantee good and consistent results.     

电动车动态测试仪表的研究

电动车作为一种新颖的清洁代步工具，由于其兼具低噪声、无污染、轻松便、价格适中等特点，越来越受到广大消费者的关注。本文分析了电动车的性能参数及其相互关系，重点介绍了电动车的动态测试方法，并给出了实现途径。     

Monte Carlo analysis of a new model-based method for insulin sensitivity testing

Insulin resistance (IR), or low insulin sensitivity, is a major risk factor in the pathogenesis of type 2 diabetes and cardiovascular disease. A simple, high resolution assessment of IR would enable earlier diagnosis and more accurate monitoring of intervention effects. Current assessments are either too intensive for clinical settings (Euglycaemic Clamp, IVGTT) or have too low resolution (HOMA, fasting glucose/insulin). Based on high correlation of a model-based measure of insulin sensitivity and the clamp, a novel, clinically useful test protocol is designed with: physiological dosing, short duration (<1 h), simple protocol, low cost and high repeatability. Accuracy and repeatability are assessed with Monte Carlo analysis on a virtual clamp cohort (N=146). Insulin sensitivity as measured by this test has a coefficient of variation (CV) of CV(SI)=4.5% (90% CI: 3.8-5.7%), slightly higher than clamp ISI (CV(ISI)=3.3% (90% CI: 3.0-4.0%)) and significantly lower than HOMA (CV(HOMA)=10.0% (90% CI: 9.1-10.8%)). Correlation to glucose and unit normalised ISI is r=0.98 (90% CI: 0.97-0.98). The proposed protocol is simple, cost effective, repeatable and highly correlated to the gold-standard clamp.     

基于关键字驱动的自动化测试研究与实现

为有效解决目前回归测试测试效率低以及手工测试操作复杂的问题,研究了自动化测试技术的原理和方法,提出了一种基于关键字驱动的自动化测试方法;介绍了Web自动化测试实施的过程和自动化测试框架的主要功能.在遵从保证产品质量和提高测试效率的原则基础上,对系统进行自动化测试的可行性分析,再进行自动化测试开发工具的选择、制作Demo和自动化测试框架开发,实现了以关键字驱动为核心,Ruby为开发语言的自动化测试框架.实验结果表明,该方法提高了回归测试效率和测试系统的可移植性.     

Analysis of distance functions for similarity-based test suite reduction in the context of model-based testing

Test suite reduction strategies aim to produce a smaller and representative suite that presents the same coverage as the original one but is more cost-effective. In the model-based testing (MBT) context, reduction is crucial since automatic generation algorithms may blindly produce several similar test cases. In order to define the degree of similarity between test cases, researchers have investigated a number of distance functions. However, there is still little or no knowledge on whether and how they influence on the performance of reduction strategies, particularly when considering MBT practices. This paper investigates the effectiveness of distance functions in the scope of a MBT reduction strategy based on the similarity degree of test cases. We discuss six distance functions and apply them to three empirical studies. The first two studies are controlled experiments focusing on two real-world applications (and real faults) and ten synthetic specifications automatically generated from the configuration of each application (and faults randomly generated). In the third study, we also apply the reduction strategy to two subsequent versions of an industrial application by considering real faults detected. Results show that the choice of a distance function has little influence on the size of the reduced test suite. However, as reduced suites are different depending on the distance function applied, the choice can significantly affect the fault coverage. Moreover, it can also affect the stability of the reduction strategy regarding coverage of different sets of faults on different executions.     

高频窄脉冲探头在从动盘超声检测中的应用

在汽车离合器从动盘电子束焊缝自动超声检测中,从动盘厚度薄、焊缝窄、缺陷微小,检测时易出现上下界面反射波混叠,界面回波与缺陷回波难以识别等现象。为提高超声检测系统的分辨率、灵敏度和信噪比,讨论了探头参数对检测结果影响,确定探头参数,并研制出了专用于从动盘超声检测的高频窄脉冲探头。参照欧洲标准对专用探头进行频率特性测试,测试结果表明:专用探头的频率特性明显优于普通探头,更有利于提高检测系统的灵敏度、分辨率和信噪比。在实际生产中将专用探头应用于从动盘自动超声检测中,可有效检测出当量尺寸约为0.4 mm的微小缺陷,并满足检测要求。     

Multiple model-based reinforcement learning

We propose a modular reinforcement learning architecture for nonlinear, nonstationary control tasks, which we call multiple model-based reinforcement learning (MMRL). The basic idea is to decompose a complex task into multiple domains in space and time based on the predictability of the environmental dynamics. The system is composed of multiple modules, each of which consists of a state prediction model and a reinforcement learning controller. The "responsibility signal," which is given by the softmax function of the prediction errors, is used to weight the outputs of multiple modules, as well as to gate the learning of the prediction models and the reinforcement learning controllers. We formulate MMRL for both discrete-time, finite-state case and continuous-time, continuous-state case. The performance of MMRL was demonstrated for discrete case in a nonstationary hunting task in a grid world and for continuous case in a nonlinear, nonstationary control task of swinging up a pendulum with variable physical parameters.     

基于模型的海浪模拟仿真

在计算机图形学领域,自然景物的模拟一直都是一项研究重点.由于不规则性和随机性,要对水波特别是波浪进行逼真地模拟较为困难.研究了借助几何模型模拟自然界中海浪的运动,通过图形学中的真实感处理,得到逼真的海浪波动效果.在研究过程中首先是建立简单的海浪模型,通过辅助衰减函数将其改进,以符合海浪运动的物理规律,同时加入了动态变化的影响因子以增加海浪运动的随机性.结果表明,该模型可以较为真实的模拟海浪的基本运动.     

Interactive model-based vehicle tracking

This paper describes an interactive model-based vision system for vehicle tracking. A human specifies a limited amount of information in the form of object models, which establish a context for autonomous interpretation of scenes containing moving vehicles. Results are presented from several image sequences shot with hand-held uncalibrated cameras     

Immune model-based fault diagnosis

In this paper, a novel approach to immune model-based fault diagnosis methodology for nonlinear systems is presented. The diagnosis scheme consists of forward/inverse immune model identification, filtered residual generation, the fault alarm concentration (FAC), and the artificial immune regulation (AIR). A two-link manipulator simulation was employed to validate the effectiveness and robustness of the diagnosis approach. The simulation results show that it can detect and isolate actuator faults, sensor faults, and system component faults efficiently.