Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

Test Case Prioritization(TCP)techniques perform better than other regression test optimization techniques including Test Suite Reduction(TSR)and Test Case Selection(TCS).Many TCP techniques are available,and their performance is usually measured through a metric Average Percentage of Fault Detection(APFD).This metric is value-neutral because it only works well when all test cases have the same cost,and all faults have the same severity.Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results.Therefore,using the right metric for performance evaluation of TCP techniques is very important to get reliable and correct results.In this paper,two value-based TCP techniques have been introduced using Genetic Algorithm(GA)including Value-Cognizant Fault Detection-Based TCP(VCFDB-TCP)and Value-Cognizant Requirements Coverage-Based TCP(VCRCB-TCP).Two novel value-based performance evaluation metrics are also introduced for value-based TCP including Average Percentage of Fault Detection per value(APFDv)and Average Percentage of Requirements Coverage per value(APRCv).Two case studies are performed to validate proposed techniques and performance evaluation metrics.The proposed GA-based techniques outperformed the existing state-of-the-art TCP techniques including Original Order(OO),Reverse Order(REV-O),Random Order(RO),and Greedy algorithm.

An Optimized Test Case Minimization Technique Using Genetic Algorithm for Regression Testing

Regression testing is a widely used approach to confirm the correct functionality of the software in incremental development.The use of test cases makes it easier to test the ripple effect of changed requirements.Rigorous testingmay help in meeting the quality criteria that is based on the conformance to the requirements as given by the intended stakeholders.However,a minimized and prioritized set of test cases may reduce the efforts and time required for testingwhile focusing on the timely delivery of the software application.In this research,a technique named Test Reduce has been presented to get a minimal set of test cases based on high priority to ensure that the web applicationmeets the required quality criteria.A new technique TestReduce is proposed with a blend of genetic algorithm to find an optimized and minimal set of test cases.The ultimate objective associated with this study is to provide a technique that may solve the minimization problem of regression test cases in the case of linked requirements.In this research,the 100-Dollar prioritization approach is used to define the priority of the new requirements.

A hybrid algorithm based on ILP and genetic algorithm for time-aware test case prioritization

To solve the problem of time-awarc test case prioritization,a hybrid algorithm composed of integer linear programming and the genetic algorithm(ILP-GA)is proposed.First,the test case suite which cm maximize the number of covered program entities a d satisfy time constraints is selected by integer linea progamming.Secondly,the individual is encoded according to the cover matrices of entities,and the coverage rate of program entities is used as the fitness function and the genetic algorithm is used to prioritize the selected test cases.Five typical open source projects are selected as benchmark programs.Branch and method are selected as program entities,and time constraint percentages a e 25%and 75%.The experimental results show that the ILP-GA convergence has faster speed and better stability than ILP-additional and IP-total in most cases,which contributes to the detection of software defects as early as possible and reduces the software testing costs.

User Session-Based Test Case Generation and Optimization Using Genetic Algorithm

An approach to generating and optimizing test cases is proposed for Web application testing based on user sessions using genetic algorithm. A large volume of meaningful user sessions are obtained after purging their irrelevant information by analyzing user logs on the Web server. Most of the redundant user sessions are also removed by the reduction process. For test reuse and test concurrency, it divides the user sessions obtained into different groups, each of which is called a test suite, and then prioritizes the test suites and the test cases of each test suite. So, the initial test suites and test cases, and their initial executing sequences are achieved. However, the test scheme generated by the elementary prioritization is not much approximate to the best one. Therefore, genetic algorithm is employed to optimize the results of grouping and prioritization. Meanwhile, an approach to generating new test cases is presented using crossover. The new test cases can detect faults caused by the use of possible conflicting data shared by different users.

An Automated Approach to Generate Test Cases From Use Case Description Model

Test complexity and test adequacy are frequently raised by software developers and testing agents.However,there is little research works at this aspect on specification-based testing at the use case description level.Thus,this research proposes an automatic test cases generator approach to reduce the test complexity and to enhance the percentage of test coverage.First,to support the infrastructure for performing automatic,this proposed approach refines the use cases using use case describing template and save it in the text file.Then,the saved file is input to the Algorithm of Control Flow Diagram(ACFD)to convert use case details to a control flow diagram.After that,the Proposed Tool of Generating Test Paths(PTGTP)is used to generate test cases from the control flow diagram.Finally,the genetic algorithm associated with transition coverage is adapted to optimize and evaluate the adequacy of such test cases.A money withdrawal use case in the ATM system is used as an ongoing case study.Preliminary results show that the generated test cases achieve high coverage with an optimal test case.This automatic test case generation approach is effective and efficient.Therefore,it could promote to use other test case coverage criteria.

Implementation of Hybrid Particle Swarm Optimization for Optimized Regression Testing

Software test case optimization improves the efficiency of the software by proper structure and reduces the fault in the software.The existing research applies various optimization methods such as Genetic Algorithm,Crow Search Algorithm,Ant Colony Optimization,etc.,for test case optimization.The existing methods have limitations of lower efficiency in fault diagnosis,higher computa-tional time,and high memory requirement.The existing methods have lower effi-ciency in software test case optimization when the number of test cases is high.This research proposes the Tournament Winner Genetic Algorithm(TW-GA)method to improve the efficiency of software test case optimization.Hospital Information System(HIS)software was used to evaluate TW-GA model perfor-mance in test case optimization.The tournament Winner in the proposed method selects the instances with the best fitness values and increases the exploitation of the search to find the optimal solution.The TW-GA method has higher exploita-tion that helps to find the mutant and equivalent mutation that significantly increases fault diagnosis in the software.The TW-GA method discards the infor-mation with a lower fitness value that reduces the computational time and mem-ory requirement.The TW-GA method requires 5.47 s and the MOCSFO method requires 30 s for software test case optimization.

A Parallel Genetic Algorithm Based on Spark for Pairwise Test Suite Generation

Pairwise testing is an effective test generation technique that requires all pairs of parameter values to be by at least one test case. It has been proven that generating minimum test suite is an NP-complete problem covered Genetic algorithms have been used for pairwise test suite generation by researchers. However, it is always a time-consuming process, which leads to significant limitations and obstacles for practical use of genetic algorithms towards large-scale test problems. Parallelism will be an effective way to not only enhance the computation performance but also improve the quality of the solutions. In this paper, we use Spark, a fast and general parallel computing platform, to parallelize the genetic algorithm to tackle the problem. We propose a two-phase parallelization algorithm including fitness evaluation parallelization and genetic operation parallelization. Experimental results show that our algorithm outperforms the sequential genetic algorithm and competes with other approaches in both test suite size and computational performance. As a result, our algorithm is a promising improvement of the genetic algorithm for pairwise test suite generation.

基于GA-BP神经网络的大型客机气流角估计方法

为了解决硬件冗余难以克服的气流角传感器共因故障问题,进一步提高飞机气流角信号的可靠性,研究了基于GABP神经网络的气流角估计方法。通过BP神经网络融合姿态角、加速度、风速等参数来实现不依赖气流角传感器的气流角估计;引入遗传算法对神经网络权值和阈值进行全局优化,提高估计精度;对某大型客机的试飞数据预处理后用于模型的训练和测试。仿真结果表明,训练完成的GA-BP神经网络模型对气流角的估计值贴近实际值,稳定性和精度明显高于BP神经网络。上述方法给飞机增加一个余度的气流角信号,可用于传感器故障时为飞机提供可靠的气流角信号。

一种基于Messy GA的结构测试数据自动生成方法

结构性测试是标识测试用例的基本方法之一.由于程序语言的复杂性以及被测程序的多样性,自动生成结构测试数据的一种有效方法是根据程序运行结果指导生成过程,通过不断迭代,生成符合要求的测试数据集.提出一种基于MessyGA的结构测试数据自动生成方法,将测试覆盖率表示为测试输入集X的函数F(X),并利用MessyGA不需要染色体模式排列的先验知识即可进行优化求解的性质对F(X)进行迭代寻优,进一步提高了搜索的并行性,并最终提高测试覆盖率.对一组标准测试程序和若干实际应用程序的实验结果表明,较之现有基于遗传算法的生成方法,该方法能够以更高的效率生成更高质量的测试数据,并适用于较大规模的程序.

基于遗传算法的DM-GA组合测试数据生成方法

测试数据生成是组合软件测试的重要部分,生成高质量的测试数据对于软件测试具有重要意义。针对两两组合测试数据生成问题,结合传统遗传算法,加入了精英策略和自适应变异概率,提出了DM-GA(dynamic mutation rates-genetic algorithm)算法,改善了传统遗传算法容易陷入局部最优以及收敛速度慢等不足,并取得了良好的效果。实验结果表明DM-GA算法可以作为一种较理想的两两组合测试数据生成方法。

基于LSGA的最小测试用例集自动生成

测试数据的生成是一个复杂的问题且其技术和方法还不成熟.根据实现语句覆盖的测试目标,提出了最大稳定遗传算法(LSGA).该算法充分考虑了遗传算法的稳定性并在构造适应度函数和路径编号时提出了"邻近者优先"原则和"就近路径编号"原则.这个算法可以生成满足测试目标的最小用例集且其性能明显优于遗传算法.

基于GASA的最小测试集求取的研究

近年来发展的离散事件系统(DES)理论可提供一种统一的对数模混合电路中数字电路和模拟电路测试都有效的方法。对基于DES理论的可测试性研究中电路最小测试集的求取问题,提出了一种运用GASA混合策略的组合优化方法,并对进一步的研究工作进行了展望。

基于GA的建筑物振动多频率成分辨识

基于频谱分析的建筑物振动信号的多频率成分分析方法是一种近似方法,其误差较大,为此提出一种基于遗传算法的新方法——GA辨识法。为了说明GA辨识法相对于频谱辨识法的优越性及其用于建筑物振动信号多频率成分分析的可行性,采用一组模拟信号(无噪声以及加入随机白噪声)以及一组建筑物振动实际观测数据,用两种方法分别进行信号的多频率成分分析并且对结果进行比较。比较结果说明,GA辨识法优于频谱辨识法。

基于OFGA的软件测试用例自动生成

为提高测试用例生成的质量和效率,提出一种基于最优家族遗传算法(OFGA)的软件测试用例自动生成新算法。基于OFGA的测试用例生成算法在执行过程中适当缩小搜索区域,从而在相对更小的区域内快速寻找最优解。因此,OFGA能比较快地加速算法的收敛,提高算法的效率,在测试用例的生成上具有较大的应用潜力。由实验结果可知,新算法比遗传算法(GA)在测试用例自动生成上耗时更少,效果更佳。

基于GATS算法的面向对象测试用例自动生成

在遗传算法的基础上,引入禁忌搜索算法,提出了一种面向对象测试用例自动生成的方法.该方法设计了一种新的类对象编码方式,并在此基础上构造了类测试用例自动生成所需的适应度函数,使每一个测试用例在局部区域中再次寻找最优值,从而改进整体算法搜索最佳值的能力.实验结果表明,该方法结合遗传群体优化和禁忌搜索较强的爬山能力,能够实现快速全局优化,自动生成高质量的测试用例.

基于GA的多路径测试数据生成器适配函数设计

提出了基于GA的多路径测试数据生成的概念和实现方法。讨论了为了实现多路径测试数据生成,设计有效的和高效的适配函数应考虑的因素,用实际案例针对提出的适配函数进行了功能性能验证。结果表明了适配函数设计的有效性。

基于GA-BP的汽车行李箱盖内板冲压成形工艺优化

为了更快捷准确地解决冲压成形过程中的工艺参数优化问题,提出一种DYNAFORM与智能算法相融合的优化策略。以汽车行李箱盖内板冲压成形为例,以最大减薄率为优化目标,采用CAD建立三维模型,并利用有限元分析软件DYNAFORM进行数值模拟;设计正交试验并对试验结果进行极差分析,分析成形过程中摩擦系数、压边力、模具间隙和冲压速度对最大减薄率的影响规律并初步确定一组优化方案;设计拉丁超立方抽样,在选定的各工艺参数范围内均匀抽取60组样本点;构建GA-BP神经网络模型,运用拉丁超立方抽样的模拟数据对其进行训练,得到各工艺参数和优化目标的非线性映射关系;最后用遗传算法函数寻优获得一组最优工艺参数组合,进行数值模拟对比验证,验证了该优化方案的合理性与准确性。

时间Petri网与GA-PSO算法相结合的并行测试

并行测试任务调度方案在自动测试系统中一直是尚未解决的难题。基于Petri网理论的基础,建立了并行测试的时间Petri网模型,并且首次将遗传-粒子群优化(GA-PSO)算法引入到时间Petri网的变迁序列的寻找过程中,快速地求得了最优调度方案。仿真结果表明,本算法能够以较大的收敛概率快速地收敛,最终得到最优变迁序列。