基于信息熵的电路测点优化策略

针对电路测试点优化问题,研究了相关性模型和信息熵理论,在对相关性模型进行改进的基础上,建立了基于信息熵的电路测点优化策略,给出了具体的计算步骤。应用实例表明,该方法是有效的,可应用于模拟、数字和混合信号电路的故障检测及可测试性设计。     

A cluster analysis selection strategy for supersaturated designs

Supersaturated designs (SSDs) are widely researched because they can greatly reduce the number of experiments. However, analyzing the data from SSDs is not easy as their run size is not large enough to estimate all the main effects. This paper introduces contrast-orthogonality cluster and anticontrast-orthogonality cluster to reflect the inner structure of SSDs which are helpful for experimenters to arrange factors to the columns of SSDs. A new strategy for screening active factors is proposed and named as contrast-orthogonality cluster analysis (COCA) method. Simulation studies demonstrate that this method performs well compared to most of the existing methods. Furthermore, the COCA method has lower type II errors and it is easy to be understood and implemented.     

基于类信息的文本聚类中特征选择算法

文本聚类属于无监督的学习方法,由于缺乏类信息还很难直接应用有监督的特征选择方法,因此提出了一种基于类信息的特征选择算法,此算法在密度聚类算法的聚类结果上使用信息增益特征选择法重新选择最有分类能力的特征,实验验证了算法的可行性和有效性。     

A set of complexity metrics for guiding the software test process

Software verification and validation is a domain which is covered by many dynamic test, static analysis, and formal verification techniques. This presents a problem to practitioners with respect to selecting those suitable techniques which can be used successfully. The basic idea of the methodology presented here is to select test techniques which fit the software under test. A dynamic test technique requires that certain program elements are covered, will be sensitive to errors associated with these elements, because executing an error location is a precondition for revealing the error. Furthermore, it is likely that the probability of errors increases with complexity. Complexity can be characterized in terms of several properties which can be used to suggest various testing strategies. The complexity of the various software properties can be measured using appropriate complexity metrics. Properties with unusual high complexity measures should be tested very throughly. The approach described in this paper permits the selection of test techniques based on the values of the metrics with respect to a particular software product.     

Using symbolic execution and data flow criteria to aid test data selection

The utilization is described of a data flow path selection criterion in a symbolic execution system. The system automatically generates a subset of program paths according to a certain control flow criterion. This subset is called the ZOT-subset, since it requires paths that traverse loops zero, one and two times. Experience indicates that traversing this subset of program paths is enough to cover most control flow and data flow components of the program. The problem with the ZOT-subset is that it might contain, for large programs, a large number of paths. The number of paths in this subset can be reduced by concentrating on executable paths that cover vital components of programs such as data flow components. This object is achieved by employing a data flow path selection criterion in the system. The system symbolically executes the paths of the ZOT-subset, and creates a system of branch conditions for each one. The user determines infeasible paths by checking the consistency of each system of conditions. The system selects feasible paths from the ZOT-subset that cover the data flow criterion. Solving the systems of conditions of the selected paths provides the user with test data to fulfil the given data flow criterion.     

Framework for evaluation and selection of the software packages: A hybrid knowledge based system approach

Evaluation and selection of the software packages is complicated and time consuming decision making process. Selection of inappropriate software package can turn out to be costly and adversely affects business processes and functioning of the organization. In this paper we describe (i) generic methodology for software selection, (ii) software evaluation criteria, and (iii) hybrid knowledge based system (HKBS) approach to assist decision makers in evaluation and selection of the software packages. The proposed HKBS approach employs an integrated rule based and case based reasoning techniques. Rule based reasoning is used to capture user needs of the software package and formulate a problem case. Case based reasoning is used to retrieve and compare candidate software packages with the user needs of the package. This paper also evaluates and compares HKBS approach with the widely used existing software evaluation techniques such as analytic hierarchy process (AHP) and weighted scoring method (WSM).     

ContextAug: model-domain failing test augmentation with contextual information

In the process of software development, the ability to localize faults is crucial for improving the efficiency of debugging. Generally speaking, detecting and repairing errant behavior at an early stage of the development cycle considerably reduces costs and development time. Researchers have tried to utilize various methods to locate the faulty codes. However, failing test cases usually account for a small portion of the test suite, which inevitably leads to the class-imbalance phenomenon and hampers the effectiveness of fault localization. Accordingly, in this work, we propose a new fault localization approach named ContextAug. After obtaining dynamic execution through test cases, ContextAug traces these executions to build an information model; subsequently, it constructs a failure context with propagation dependencies to intersect with new model-domain failing test samples synthesized by the minimum variability of the minority feature space. In contrast to traditional test generation directly from the input domain, ContextAug seeks a new perspective to synthesize failing test samples from the model domain, which is much easier to augment test suites. Through conducting empirical research on real large-sized programs with 13 state-of-the-art fault localization approaches, ContextAug could significantly improve fault localization effectiveness with up to 54.53%. Thus, ContextAug is verified as able to improve fault localization effectiveness.     

A dynamic stochastic model for automatic grammar‐based test generation

Grammar‐based test generation provides a systematic approach to producing test cases from a given context‐free grammar. Unfortunately, naive grammar‐based test generation is problematic because of the fact that exhaustive random test case production is often explosive, and grammar‐based test generation with explicit annotation controls often causes unbalanced testing coverage. In this paper, we present an automatic grammar‐based test generation approach, which takes a symbolic grammar as input, requires zero control input from users, and produces well‐distributed test cases. Our approach utilizes a novel dynamic stochastic model where each variable is associated with a tuple of probability distributions, which are dynamically adjusted along the derivation. We further present a coverage tree illustrating the distribution of generated test cases and their detailed derivations. More importantly, the coverage tree supports various implicit derivation control mechanisms. We implemented this approach in a Java‐based system, named Gena. Each test case generated by Gena automatically comes with a set of structural features, which can play an important and effective role on automated failure causes localization. Experimental results demonstrate the effectiveness of our approach, the well‐balanced distribution of generated test cases over grammatical structures, and a case study on grammar‐based failure causes localization. Copyright © 2014 John Wiley & Sons, Ltd.     

IPSETFUL: an iterative process of selecting test cases for effective fault localization by exploring concept lattice of program spectra

Fault localization is an important and challenging task during software testing. Among techniques studied in this field, program spectrum based fault localization is a promising approach. To perform spectrum based fault localization, a set of test oracles should be provided, and the effectiveness of fault localization depends highly on the quality of test oracles. Moreover, their effectiveness is usually affected when multiple simultaneous faults are present. Faced with multiple faults it is difficult for developers to determine when to stop the fault localization process. To address these issues, we propose an iterative fault localization process, i.e., an iterative process of selecting test cases for effective fault localization (IPSETFUL), to identify as many faults as possible in the program until the stopping criterion is satisfied. It is performed based on a concept lattice of program spectrum (CLPS) proposed in our previous work. Based on the labeling approach of CLPS, program statements are categorized as dangerous statements, safe statements, and sensitive statements. To identify the faults, developers need to check the dangerous statements. Meantime, developers need to select a set of test cases covering the dangerous or sensitive statements from the original test suite, and a new CLPS is generated for the next iteration. The same process is proceeded in the same way. This iterative process ends until there are no failing tests in the test suite and all statements on the CLPS become safe statements. We conduct an empirical study on several subject programs, and the results show that IPSETFUL can help identifymost of the faults in the program with the given test suite. Moreover, it can save much effort in inspecting unfaulty program statements compared with the existing spectrum based fault localization techniques and the relevant state of the art technique.     

面向软件错误定位与理解的测试执行离散特征筛选

软件错误定位与错误理解是软件调试过程中的重要步骤,然而调试人员利用基于覆盖分析的软件错误定位获取的可疑度,从高到低静态分析每条程序语句的检查方式,与实际软件调试过程并不相符。为了能够筛选更有助于理解错误根源的测试执行,尤其是致使程序失效的失效执行,帮助调试人员进行动态差异化分析,针对失效执行提出基于高可疑度覆盖率、揭示错误潜力和覆盖语句可疑度离散特征的3种优先级策略,针对成功执行提出加权余弦相似度匹配策略。通过将3种失效执行优先级策略与随机选择在常用错误定位技术中进行实验对比,验证了基于覆盖语句可疑度离散特征的失效执行筛选策略能够对筛选前后的错误理解工作量变化产生更强的积极影响和更弱的消极影响,并能够在相同工作量下理解更多的错误,进而更有助于将错误定位结果应用于错误根源的理解。     

Impediments for software test automation: A systematic literature review

Automated software testing is a critical enabler for modern software development, where rapid feedback on the product quality is expected. To make the testing work well, it is of high importance that impediments related to test automation are prevented and removed quickly. An enabling factor for all types of improvement is to understand the nature of what is to be improved. We have performed a systematic literature review of reported impediments related to software test automation to contribute to this understanding. In this paper, we present the results from the systematic literature review: The list of identified publications, a categorization of identified impediments, and a qualitative discussion of the impediments proposing a socio‐technical system model of the use and implementation of test automation.     

一种新型的故障检测定位方法研究

针对大型复杂系统故障定位耗时长等问题,在分析故障树特征的基础上,改进了遗传算法,设计了基于故障树底事件的故障检测定位方法。该方法将顺序检测融入到改进的遗传算法检测当中,在工程应用领域体现了一定的优越性。仿真实验结果表明,该方法能够准确定位故障,有助于提高故障检测定位的效率。     

A knowledge-based system for conveyor equipment selection

Conveyor equipment selection is a complex, and sometimes, tedious task since there are literally hundreds of equipment types and manufacturers to choose from. The expert system approach to conveyor selection provides advantages of unbiased decision making, greater availability, faster response, and reduced cost as compared to human experts. This paper discusses the development of a prototype expert system for industrial conveyor selection. The system, which was developed on Level V Object, provides the user with a list of conveyor solutions for their material handling needs along with a list of suppliers for the suggested conveyor devices. Conveyor types are selected on the basis of a suitability score, which is a measure of the fulfillment of the material handling requirements by the characteristics of the conveyor. The computation of the score is performed through the Weighted Evaluation Method, and the Expected Value Criterion for decision making under risk. The prototype system was successfully validated through two industrial case studies.     

A dependency-based search strategy for feature selection

Feature selection has become an increasingly important field of research. It aims at finding optimal feature subsets that can achieve better generalization on unseen data. However, this can be a very challenging task, especially when dealing with large feature sets. Hence, a search strategy is needed to explore a relatively small portion of the search space in order to find “semi-optimal” subsets. Many search strategies have been proposed in the literature, however most of them do not take into consideration relationships between features. Due to the fact that features usually have different degrees of dependency among each other, we propose in this paper a new search strategy that utilizes dependency between feature pairs to guide the search in the feature space. When compared to other well-known search strategies, the proposed method prevailed.     

基于测点必要度的模拟电路测点优选方法

针对模拟电路的测点选择问题,根据测点的必要程度,提出了“测点必要度”的概念,同时结合“故障隔离度”的概念,建立了新的测点优选方法,给出了新算法的详细计算过程和应用实例．实验分析结果表明,新算法的计算效果优于熵算法和基于故障隔离度的测点选择算法,可用于复杂模拟电路的测点选择．     

A SAS macro for a clustered logrank test

The clustered logrank test is a nonparametric method of significance testing for correlated survival data. Examples of its application include cluster randomized trials where groups of patients rather than individuals are randomized to either a treatment or a control intervention. We describe a SAS macro that implements the 2-sample clustered logrank test for data where the entire cluster is randomized to the same treatment group. We discuss the theory and applications behind this test as well as details of the SAS code.     

Regression test selection for C++ software

Regression testing is an important but expensive software maintenance activity performed with the aim of providing confidence in modified software. Regression test selection techniques reduce the cost of regression testing by selecting test cases for a modified program from a previously existing test suite. Many researchers have addressed the regression test selection problem for procedural language software, but few have addressed the problem for object‐oriented software. This paper presents a regression test selection technique for use with object‐oriented software. The technique constructs graph representations for software, and uses these graphs to select test cases, from the original test suite, that execute code that has been changed for the new version of the software. The technique is strictly code based, and requires no assumptions about the approach used to specify or test the software initially. The technique applies to modified and derived classes, and to application programs that use modified classes. Copyright © 2000 John Wiley & Sons, Ltd.     

Dynamic method for software test data generation

Test data generation in program testing is the process of identifying a set of test data which satisfies a given testing criterion. Existing pathwise test data generators proceed by selecting program paths that satisfy the selected criterion and then generating program inputs for these paths. One of the problems with this approach is that unfeasible paths are often selected; as a result, significant computational effort can be wasted in analysing those paths. In this paper, an approach to test data generation, referred to as a dynamic approach for test data generation, is presented. In this approach, the path selection stage is eliminated. Test data are derived based on the actual execution of the program under test and function minimization methods. The approach starts by executing a program for an arbitrary program input. During program execution for each executed branch, a search procedure decides whether the execution should continue through the current branch or an alternative branch should be taken. If an undesirable execution flow is observed at the current branch, then a real-valued function is associated with this branch, and function minimization search algorithms are used to locate values of input variables automatically, which will change the flow of execution at this branch.     

面向有效错误定位的测试用例优选方法

针对已有测试用例选择方法在提高错误定位有效性方面存在局限性的问题,首先,定义“失效覆盖向量相似度优先排序”准则,将执行路径与失效执行路径相似的成功测试用例赋予较高的优先级;然后定义“失效覆盖等价划分优化选择”准则,选择能够最大区分失效执行语句的成功测试用例集合;在此基础上,建立测试用例优选模型(effective selection, ES).不同于已有方法,ES充分利用失效执行路径来提高错误定位的有效性.该模型被应用于优选Siemens测试用例集合,其结果被应用于Tarantula等4种错误定位方法.结果表明,ES在约简率Reduction和衡量错误定位有效性的Expense_increase两个指标方面,均优于已有的基于语句和基于向量的测试用例约简方法.ES不但可以获得97%以上的约简率,提高错误定位的效率,而且具有较低的Expense_increase,显著提高了错误定位的有效性.