Method for test case selection and execution of web application regression testing

In order to improve the efficiency of regression testing in web application,the control flow graph and the greedy algorithm are adopted.This paper considers a web page as a basic unit and introduces a test case selection method for web application regression testing based on the control flow graph.This method is safe enough to the test case selection.On the base of features of request sequence in web application,the minimization technique and the priority of test cases are taken into consideration in the process of execution of test cases in regression testing for web application.The improved greedy algorithm is also raised resulting in optimization of execution of test cases.The experiments indicate that the number of test cases which need to be retested is reduced,and the efficiency of execution of test cases is also improved.

Component system regression testing method based on CTAM

The emphasis of component system regression testing is retesting of the event interaction between updated components and other components in a system.A component system regression testing method based on a new component testing association model （CTAM） is proposed.First,the modification-affected component groups are identified by the impact analysis on CTAM,and each component in this group is assigned with an influence degree.Then,previous test cases are selected according to the influence degree,to generate the minimal regression test suite.Compared with traditional methods,CTAM is derived from the statistic on the interactive events that occurred in previous test executions,and focuses on the complicated relationship between components,which is more applicable to the component system regression testing.

Research of the Control Domain of Edges in Regression Testing

Regression testing is the process of validating modified software to provide confidence that the changed parts of the software behave as intended and that the unchanged parts have not been adversely affected by the modifications. The goal of regression testing is to reduce the test suit by testing the new characters and the modified parts of a program with the original test suit. Regression testing is a high cost testing method. This paper presents a regression testing selection technique that can reduce the test suit on the basis of Control Flow Graph (CFG). It import the inherit strategy of object-oriented language to ensure an edge’s control domain to reduce the test suit size effectively. We implement the idea by coding the edge. An algorithm is also presented at last.

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.

Improved Test Case Selection Algorithm to Reduce Time in Regression Testing

Regression testing(RT)is an essential but an expensive activity in software development.RT confirms that new faults/errors will not have occurred in the modified program.RT efficiency can be improved through an effective technique of selected only modified test cases that appropriate to the modifications within the given time frame.Earlier,several test case selection approaches have been introduced,but either these techniques were not sufficient according to the requirements of software tester experts or they are ineffective and cannot be used for available test suite specifications and architecture.To address these limitations,we recommend an improved and efficient test case selection(TCS)algorithm for RT.Our proposed technique decreases the execution time and redundancy of the duplicate test cases(TC)and detects onlymodified changes that appropriate to themodifications in test cases.To reduce execution time for TCS,evaluation results of our proposed approach are established on fault detection,redundancy and already executed test case.Results indicate that proposed technique decreases the inclusive testing time of TCS to execute modified test cases by,on average related to a method of Hybrid Whale Algorithm(HWOA),which is a progressive TCS approach in regression testing for a single product.

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.

Regression Testing in Developer Environment for Absence of Code Coverage

The techniques of test case prioritization schedule the execution order of test cases to attain respective target, such as enhanced level of forecasting the fault. The requirement of the prioritization can be viewed as the en-route for deriving an order of relation on a given set of test cases which results from regression testing. Alteration of programs between the versions can cause more test cases which may respond differently to following versions of software. In this, a fixed approach to prioritizing test cases avoids the preceding drawbacks. The JUnit test case prioritization techniques operating in the absence of coverage information, differs from existing dynamic coverage-based test case prioritization techniques. Further, the prioritization test cases relying on coverage information were projected from fixed structures relatively other than gathered instrumentation and execution.

Drug Sensitivity Test and Regression Verification of Escherichia coli from Rex Rabbit

[ Objectives] The paper aimed to select drugs reasonably for treatment of rex rabbit colibacillosis, and to isolate the pathogenicity of Escherichia coll. [ Methods ] Pathogen isolation, drug sensitivity test and pathogen regression test were performed with rex rabbits killed by E. coli in clinic. [ Results] The isolate was E. coli 0-23, susceptible to amikacin and cefotaxime sodium; when the challenge dose was 1.0 mL/rabbit （about one billion E. coli）, the test animal would discharge mucous feces. [ Conclusions] The results provided model support for clinical medicine selection against rex rabbit colibacillosis.

Use of Random Regression Test-Day Model to Estimate Genetic Parameters of Milk Yield in Holstein Cows

(Co) variance components and genetic parameters were estimated for milk yield of Iranian Holstein cows. A total number of 68,945 milk test-day records of first, second and third lactations of 8515 animals from 100 sires and 7743 dams originated from 34 herds collected during 2007 to 2009 by Iranian animal breeding center were used. The ASReml computer program was used to analyze the milk test-day records using the random regression procedure. Herd test date (HTD), milking times per day (milking frequency), number of lactations, year of birth, year of calving, age of animal at calving and days in milk (DIM) considered as fixed effects and additive genetic effects and animal permanent environmental effects were considered as the random effects. Additive genetic variance, animal permanent environment variance, residual variance, phenotypic variance, heritability and repeatability were estimated during different months of lactation between 5.7 - 19.6, 15.3 - 27.1, 31.4 - 17.2, 45.8 - 64.83, 0.1 - 0.32 and 0.4 - 0.6, respectively. Genetic correlation and phenotypic correlation were also estimated between months of lactation in range of -0.35 - 0.98 and 0.03 - 0.67, respectively. Genetic correlation and phenotypic correlation both showed the same changing pattern and they decreased as the interval between months of lactation increased.

A New Test for Large Dimensional Regression Coefficients

In the article, hypothesis test for coefficients in high dimensional regression models is considered. I develop simultaneous test statistic for the hypothesis test in both linear and partial linear models. The derived test is designed for growing p and fixed n where the conventional F-test is no longer appropriate. The asymptotic distribution of the proposed test statistic under the null hypothesis is obtained.

TESTING OF CORRELATION AND HETEROSCEDASTICITY IN NONLINEAR REGRESSION MODELS WITH DBL(p,q,1) RANDOM ERRORS

Chaos theory has taught us that a system which has both nonlinearity and random input will most likely produce irregular data. If random errors are irregular data, then random error process will raise nonlinearity （Kantz and Schreiber （1997））. Tsai （1986） introduced a composite test for autocorrelation and heteroscedasticity in linear models with AR（1） errors. Liu （2003） introduced a composite test for correlation and heteroscedasticity in nonlinear models with DBL（p, 0, 1） errors. Therefore, the important problems in regression model axe detections of bilinearity, correlation and heteroscedasticity. In this article, the authors discuss more general case of nonlinear models with DBL（p, q, 1） random errors by score test. Several statistics for the test of bilinearity, correlation, and heteroscedasticity are obtained, and expressed in simple matrix formulas. The results of regression models with linear errors are extended to those with bilinear errors. The simulation study is carried out to investigate the powers of the test statistics. All results of this article extend and develop results of Tsai （1986）, Wei, et al （1995）, and Liu, et al （2003）.

Asymptomatic Distribution of Goodness-of-Fit Tests in Logistic Regression Model

The logistic regression model has been become commonly used to study the association between a binary response variable;it is widespread application rests on its easy application and interpretation. The subject of assessment of goodness-of-fit in logistic regression model has attracted the attention of many scientists and researchers. Goodness-of-fit tests are methods to determine the suitability of the fitted model. Many of methods proposed and discussed for assessing goodness-of fit in logistic regression model, however, the asymptotic distribution of goodness-of-fit statistics are less examine, it is need more investigated. This work, will focus on assessing the behavior of asymptotic distribution of goodness-of-fit tests, also make comparison between global goodness-of-fit tests, and evaluate it by simulation.

PARAMETRIC TEST IN PARTIAL LINEAR REGRESSION MODELS

Consider the regression model, n. Here the design points (xi,ti) are known and nonrandom, and ei are random errors. The family of nonparametric estimates of g() including known estimates proposed by Gasser & Muller[1] is also proposed to be a class of new nearest neighbor estimates of g(). Baed on the nonparametric regression procedures, we investigate a statistic for testing H0:g=0, and obtain some aspoptotic results about estimates.

Correlation and Regression Analyses on the Washback effects of Achievement Oral Test on College English Learning

The purpose of this study was to investigate how washback effect of a reformed test influenced students'perspectives in their spoken English learning. This study was expected to enrich the existing literature in testing washback in English as a foreign language context.Findings indicate that the adding of oral part in achievement test does have a positive washback effect on students'learning. However, such a washback effect on students'learning is quite superficial and limited.

基于正交试验的纱线断裂时间与强力的回归分析

通过三因素三水平的正交试验,利用极差法和显著性水平分析法得出:隔距、拉伸速度、预加张力这三个因素对单纱断裂强力、断裂伸长率、断裂时间的影响不明显;隔距、预加张力对单纱断裂强力、断裂伸长率、断裂时间的影响为不显著;拉伸速度对单纱断裂强力、断裂伸长率的影响为不显著,对断裂时间的影响为显著。通过回归分析,断裂时间与断裂强力两者之间存在明显的函数关系,建立断裂时间与断裂强力的回归方程,R^(2)值接近1,该方程可以准确有效地模拟断裂时间与断裂强力的函数关系。断裂时间是单纱断裂强力的一个重要表征值,建议在以后的标准更新中将其纳为考核指标,为纱线的使用者提供参考依据。

基于Gamma Test的非线性降雨径流回归模型研究

Gamma Test是一个与模型无关的数据分析方法,可以解决建立回归模型时面对的模型精度评价、划分率定和验证的数据及模型输入因子选择这三个问题。本文以英国的River Tone流域为例,应用Gamma Test方法分析数据,指导建立双层BP神经网络降雨-径流模型。结果表明,Gamma Test可以指导优化输入因子,精简模型结构,防止过拟合。

SOArTester4RT:一种面向组合服务演化的高效回归测试工具

提出了一中基于有向图关键路径选择的组合服务回归测试方法.通过分析组合服务执行流,识别受影响的执行流,选择适当的回归测试路径,并生成相应的回归测试用例.基于该方法,在SOArWare平台上实现了SOArTester4RT工具.实例分析和测试结果表明:该工具可以对BPMN的各种控制结构加以分析,产生必需的测试用例,有效提高了组合服务演化后的回归测试效率.

基于doctest的Python程序自动评测方法研究

研究了通过扩展Python标准库模块doctest进行Python程序自动评测的方法,讨论了doctest模块的内部编程模型,介绍了doctest的测试步骤,分析、总结了doctest的缺点与不足。根据教学实践环节的实际需求提出了扩展和改造doctest编程模型的思路和方法,实现了测试用例的动态部署、测试用例权重设定以及评测结果的格式化输出等重要功能。实际应用表明,该方法评测准确、运行稳定,满足较大规模场景下教学和考试的需要。

SilkTest在数据驱动技术中的应用

简述自动化回归测试工具SilkTest的主要特点及工作原理,提出一种提高回归测试自动化程度的技术,即采用数据驱动方式,使测试脚本成为可以驱动所有类似测试用例组的通用脚本,同时实现了测试执行和测试逻辑的分离,使得测试用例的修改和维护更加容易。此外,还介绍了SilkTest在数据驱动技术中的应用,简化了测试过程,提高了测试效率。

Correlations between direct and indirect strength test methods

The difficulties associated with performing direct compression strength tests on rocks lead to the development of indirect test methods for the rock strength assessment. Indirect test methods are simple, more economical, less time-consuming, and easily adaptable to the field. The main aim of this study was to derive correlations between direct and indirect test methods for basalt and rhyolite rock types from Carlin trend deposits in Nevada. In the destructive methods, point load index, block punch index, and splitting tensile strength tests are performed. In the non-destructive methods, Schmidt hammer and ultrasonic pulse velocity tests are performed. Correlations between the direct and indirect compression strength tests are developed using linear and nonlinear regression analysis methods. The results show that the splitting tensile strength has the best correlation with the uniaxial compression strength.Furthermore, the Poisson's ratio has no correlation with any of the direct and indirect test results.