基于交叉熵方法的测试用例选择策略

基于交叉熵方法提出了一种启发式划分测试用例选择策略。该策略在待测软件参数已知的条件下,以总的测试费用最小为目的,利用交叉熵方法通过调整各个子域中测试用例选择的概率来选择测试用例。     

UML模型驱动的划分测试用例生成方法研究

测试用例的自动生成是软件测试研究的主要方向之一。针对现有的UML模型驱动测试方法在测试数据生成方面存在低效、无目的性、冗余等问题,提出了基于UML活动图模型驱动的划分测试用例自动生成方法。该方法将测试场景归为五种类型,并为每种类型规划了测试数据取值范围和选择方法。构建了基于该方法的原型工具软件,提供被测软件原模型和数据约束即可生成测试用例。实验结果表明,该方法能够在不降低测试覆盖率的情况下,能生成数量少、针对性强的测试用例集。     

基于划分分析方法的测试用例生成

从基于模型的规格说明中。应用形式化方法可以产生所需的测试用例。为了从静态模型中表达的形式化约束产生测试用例，本文阐述了一种产生此测试数据的方法．同时也说明了如何使类的方法的划分分析自动进行。以厦如何建立起给定方击的粪的序列且该序列覆盖了所有必需的测试。     

基于多准则决策的软件可靠性模型选择方法

针对现有软件可靠性模型选择方法计算复杂和适用性差的问题,提出基于多准则决策的软件可靠性模型选择方法.定义了指导软件可靠性模型选择的若干准则,包括生命周期阶段准则、模型输入要求准则、模型输出要求准则、模型假设吻合准则和失效数据趋势准则,将这些准则分为确定性准则和不确定性准则,阐述了基于这些准则进行软件可靠性模型选择的算法,并实例验证了该方法的简单可行性.指出将这种方法与现有软件可靠性模型选择方法进行综合运用的研究方向.     

基于等价类划分的测试用例设计在软件评测中的应用

软件产品与项目的数量与复杂度的不断增加,对软件评测的工作效率及测试用例高覆盖率提出了挑战。本文通过等价类划分的介绍,划分原则的阐述,以及测试用例的实例设计,为软件评测提供了一种较好的应用方法。等价类划分法有助于提高软件评测人员的测试效率,即以相对少量的测试用例完成测试的高覆盖率。     

基于函数调用路径的回归测试用例选择排序方法研究

针对在回归测试过程中,因为不断修复软件中存在的缺陷所造成的测试工作量大、测试效率低等问题,论文将测试用例选择与优先级排序技术相结合,以面向函数调用的路径覆盖生成方法为基础,提出了一种面向函数调用路径（Functions Calling Path, FCP）的测试用例选择与排序方法。首先根据函数调用关系图,对程序中被修改函数与其他函数的关联性进行分析,从初始测试用例集中选择测试用例,形成回归测试用例集;然后对这些测试用例进行优先级排序,并动态地调整优先级排序结果;最后,对优先级排序结果进行再次选择,确定最小的回归测试用例集。实验结果表明,测试用例选择与排序方法对优化回归测试用例是有效的,大大减少了回归测试用例数量,降低了回归测试成本。     

构件软件回归测试用例选择策略

软构件技术虽被广泛应用于软件系统的开发中,但其测试问题并未得到很好地解决.系统构建者对外部提供的构件内部结构及其变更信息缺乏了解,很难选择出与构件变更相关的用例用于下一轮的测试.分析已有回归测试技术的不足,提出了两种改进的回归测试策略：一种是基于增强的构件版本变更信息的方法;另一种则是基于内建式测试设计的方法.通过对几个实例程序的实验分析,初步证实了所提出的方法在实际应用中的可行性与有效性.     

基于符号零压缩二叉决策图的组合测试用例生成方法

组合测试是系统测试中一种非常有效的方法,能够在保证错误检出率的前提下采用较少的测试用例来测试系统。但是,组合测试用例集构造问题的复杂度是NP完全的。给出了一种基于符号零压缩二叉决策图(Zero-suppressed Binary Decision Diagram,ZBDD)的组合测试用例生成方法。该方法首先利用ZBDD的结构特性,对测试系统进行紧凑的符号表示。然后利用ZBDD的隐式操作,结合贪心算法的思想,不断地覆盖更多的组合并缩小未覆盖组合集合,生成2～4维覆盖强度的较小测试用例集。实验证明,所提方法不仅可行而且节点开销小。     

A decision-theoretic approach to the test allocation problem in partition testing

A partition testing strategy consists of two components: a partitioning scheme which determines the way in which the program's input domain is partitioned into subdomains; and an allocation of test cases which determines the exact number of test cases selected from each subdomain. This paper investigates the problem of determining the test allocation when a particular partitioning scheme has been chosen. We show that this problem can be formulated as a classic problem of decision-making under uncertainty, and analyze several well known criteria to resolve this kind of problem. We present algorithms that solve the test allocation problem based on these criteria, and evaluate these criteria by means of a simulation experiment. We also discuss the applicability and implications of applying these criteria in the context of partition testing.     

Optimal allocation of testing effort during testing and debugging phases: a control theoretic approach

Allocation of efforts to a software development project during the testing phase is a multifaceted task for software managers. The challenges become stiffer when the nature of the development process is considered in the dynamic environment. Many software reliability growth models have been proposed in last decade to minimise the total testing-effort expenditures, but mostly under static assumption. The main purpose of this article is to investigate an optimal resource allocation plan to minimise the cost of software during the testing and operational phase under dynamic condition. An elaborate optimisation policy based on the optimal control theory is proposed and numerical examples are illustrated. This article also studies the optimal resource allocation problems for various conditions by examining the behaviour of the model parameters and also suggests policy for the optimal release time of the software. The experimental results greatly help us to identify the contribution of each selected parameter and its weight.     

A heuristic approach for allocation of data to RFID tags: A data allocation knapsack problem (DAKP)

Durable products and their components are increasingly being equipped with one of several forms of automatic identification technology such as radio frequency identification (RFID). This technology enables data collection, storage, and transmission of product information throughout its life cycle. Ideally all available relevant information could be stored on RFID tags with new information being added to the tags as it becomes available. However, because of the finite memory capacity of RFID tags along with the magnitude of potential lifecycle data, users need to be more selective in data allocation. In this research, the data allocation problem is modeled as a variant of the nonlinear knapsack problem. The objective is to determine the number of items to place on the tag such that the value of the “unexplained” data left off the tag is minimized. A binary encoded genetic algorithm is proposed and an extensive computational study is performed to illustrate the effectiveness of this approach. Additionally, we discuss some properties of the optimal solution which can be effective in solving more difficult problem instances.     

An interactive approach for multiple criteria selection problem

In this study, we develop an interactive algorithm for the multiple criteria selection problem that aims to find the most preferred alternative among a set of known alternatives evaluated on multiple criteria. We assume the decision maker (DM) has a quasi-concave value function that represents his/her preferences. The interactive algorithm selects the pairs of alternatives to be asked to the DM based on the estimated likelihood that one alternative is preferred to another. After the DM selects the preferred alternative, a convex cone is generated based on this preference information and the alternatives dominated by the cone are eliminated. Then, the algorithm updates the likelihood information for the unselected pairwise questions. The aim of the algorithm is to detect the most preferred alternative by performing as few pairwise comparisons as possible. We present the algorithm on an illustrative example problem. We also develop a mathematical model that finds the minimum number of questions that can be asked to the DM to determine the most preferred alternative under perfect information. We use the minimum number of questions to develop strategies for interactive algorithm and measure its performance.     

A preference theory approach to decision analysis in resource allocation

This paper studies the allocation of discrete resources among multiple agents from a preference theory perspective. More specifically, the paper explores the process of decision making where:

Finding failures from passed test cases: improving the pattern classification approach to the testing of mesh simplification programs

Mesh simplification programs create three‐dimensional polygonal models similar to an original polygonal model, and yet use fewer polygons. They produce different graphics even though they are based on the same original polygonal model. This results in a test oracle problem. To address the problem, our previous work has developed a technique that uses a reference model of the program under test to train a classifier. Using such an approach may mistakenly mark a failure‐causing test case as passed. It lowers the testing effectiveness of revealing failures. This paper suggests piping the test cases marked as passed by a statistical pattern classification module to an analytical metamorphic testing (MT) module. We evaluate our approach empirically using three subject programs with over 2700 program mutants. The result shows that, using a resembling reference model to train a classifier, the integrated approach can significantly improve the failure detection effectiveness of the pattern classification approach. We also explain how MT in our design trades specificity for sensitivity. Copyright © 2009 John Wiley & Sons, Ltd.     

An adaptive tabu search approach for buffer allocation problem in unreliable non-homogenous production lines

The buffer allocation problem, i.e. how much buffer storage to allow and where to place it within the line, is an important research issue in designing production lines. In this study, a novel adaptive tabu search approach is proposed for solving buffer allocation problem in unreliable and non-homogeneous production lines. The objective is to maximize the throughput of the line, which is constrained by the capacity of each buffer space and also the total buffer capacity to allocate to these spaces. Besides proposing a new strategy to tune the parameters of tabu search adaptively during the search, an experimental study is carried out to select an intelligent initial solution scheme among three alternatives so as to decrease the search effort to obtain the best solutions. The performance of the proposed approach is evaluated by computational tests and very promising results are obtained.     

An optimization approach for coupling problem of berth allocation and quay crane assignment in container terminal

This paper addresses an effective approach to solve the issue of berth allocation and quay crane assignment in a multi-user container terminal. First of all, the studied coupling problem is formulated with the interactions between berth allocation and quay crane assignment considered. Then, an evolutionary algorithm with nested loops was developed to obtain optimal solutions. The algorithm is well structured, where two inner loops are used to solve sub-problems of berth allocation and quay crane assignment respectively; an outer loop is then utilized to find an approximate solution based on the results of the two inner loops. The results of numerical experiments show that the proposed approach can improve the essential operations in container terminals.     

An approach to testing commercial embedded systems

A wide range of commercial consumer devices such as mobile phones and smart televisions rely on embedded systems software to provide their functionality. Testing is one of the most commonly used methods for validating this software, and improved testing approaches could increase these devices’ dependability. In this article we present an approach for performing such testing. Our approach is composed of two techniques. The first technique involves the selection of test data; it utilizes test adequacy criteria that rely on dataflow analysis to distinguish points of interaction between specific layers in embedded systems and between individual software components within those layers, while also tracking interactions between tasks. The second technique involves the observation of failures: it utilizes a family of test oracles that rely on instrumentation to record various aspects of a system's execution behavior, and compare observed behavior to certain intended system properties that can be derived through program analysis. Empirical studies of our approach show that our adequacy criteria can be effective at guiding the creation of test cases that detect faults, and our oracles can help expose faults that cannot easily be found using typical output-based oracles. Moreover, the use of our criteria accentuates the fault-detection effectiveness of our oracles.