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.

Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

Software needs modifications and requires revisions regularly.Owing to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free functioning.The time and cost incurred in this process,need to be reduced by the method of test case selection and prioritization.It is observed that many nature-inspired techniques are applied in this area.African Buffalo Optimization is one such approach,applied to regression test selection and prioritization.In this paper,the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization.The proposed algorithm converges in polynomial time(O(n^(2))).In this paper,the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations.An astounding 62.5%drop in size and a 48.57%drop in the runtime of the original test suite were recorded.The obtained results are compared with Ant Colony Optimization.The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities(80%),and a reduction in the overall execution time and size of the resultant test suite.The results and analysis,hence,advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.

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.

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.

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.

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.

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction(MOMPSO)

Software testing plays a pivotal role in entire software development lifecycle.It provides researchers with extensive opportunities to develop novel methods for the optimized and cost-effective test suite Although implementation of such a cost-effective test suite with regression testing is being under exploration still it contains lot of challenges and flaws while incorporating with any of the new regression testing algorithm due to irrelevant test cases in the test suite which are not required.These kinds of irrelevant test cases might create certain challenges such as code-coverage in the test suite,fault-tolerance,defects due to uncovered-statements and overall-performance at the time of execution.With this objective,the proposed a new Modified Particle Swarm optimization used for multi-objective test suite optimization.The experiment results involving six subject programs show that MOMPSO method can outer perform with respect to both reduction rate(90.78%to 100%)and failure detection rate(44.56%to 55.01%).Results proved MOMPSO outperformed the other stated algorithms.

Review of Model-Based Testing Approaches in Production Automation and Adjacent Domains—Current Challenges and Research Gaps

As production automation systems have been and are becoming more and more complex, the task of quality assurance is increasingly challenging. Model-based testing is a research field addressing this challenge and many approaches have been suggested for different applications. The goal of this paper is to review these approaches regarding their suitability for the domain of production automation in order to identify current trends and research gaps. The different approaches are classified and clustered according to their main focus which is either testing and test case generation from some form of model automatons, test case generation from models used within the development process of production automation systems, test case generation from fault models or test case selection and regression testing.

Aware Time in Bug Fix—A Novel Automatic Test Case Selection for Prioritization of Version Control

The supreme goal of the Automatic Test case selection techniques is to guarantee systematic coverage, to recognize the usual error forms and to lessen the test of redundancy. It is unfeasible to carry out all the test cases consistently. For this reason, the test cases are picked and prioritize it. The major goal of test case prioritization is to prioritize the test case sequence and finds faults as early as possible to improve the efficiency. Regression testing is used to ensure the validity and the enhancement part of the changed software. In this paper, we propose a new path compression technique (PCUA) for both old version and new version of BPEL dataset. In order to analyze the enhancement part of an application and to find an error in an enhancement part of an application, center of the tree has been calculated. Moreover in the comparative analysis, our proposed PCUA- COT technique is compared with the existing XPFG technique in terms of time consuming and error detection in the path of an enhancement part of BPEL dataset. The experimental results have been shown that our proposed work is better than the existing technique in terms of time consuming and error detection.

Automated regression test method for scientific computing libraries:Illustration with SPHinXsys

Scientific computing libraries,whether in-house or open-source,have witnessed enormous progress in both engineering and scientific research.Therefore,it is important to ensure that modifications to the source code,prompted by bug fixing or new feature development,do not compromise the accuracy and functionality that have been already validated and verified.This paper introduces a method for establishing and implementing an automatic regression test environment,using the open-source multi-physics library SPHinXsys as an illustrative example.Initially,a reference database for each benchmark test is generated from observed data across multiple executions.This comprehensive database encapsulates the maximum variation range of metrics for different strategies,including the time-averaged,ensemble-averaged,and dynamic time warping methods.It accounts for uncertainties arising from parallel computing,particle relaxation,physical instabilities,and more.Subsequently,new results obtained after source code modifications undergo testing based on a curve-similarity comparison against the reference database.Whenever the source code is updated,the regression test is automatically executed for all test cases,providing a comprehensive assessment of the validity of the current results.This regression test environment has been successfully implemented in all dynamic test cases within SPHinXsys,including fluid dynamics,solid mechanics,fluid-structure interaction,thermal and mass diffusion,reaction-diffusion,and their multi-physics couplings,and demonstrates robust capabilities in testing different problems.It is noted that while the current test environment is built and implemented for a particular scientific computing library,its underlying principles are generic and can be easily adapted for use with other libraries,achieving equal effectiveness.

Climate change characteristics of Amur River

Unusually severe weather is occurring more frequently due to global climate change. Heat waves, rainstorms, snowstorms, and droughts are becoming increasingly common all over the world, threatening human lives and property. Both temperature and precipitation are representative variables usually used to directly reflect and forecast the influences of climate change. In this study, daily data （from 1953 to 1995） and monthly data （from 1950 to 2010） of temperature and precipitation in five regions of the Amur River were examined. The significance of changes in temperature and precipitation was tested using the Mann-Kendall test method. The amplitudes were computed using the linear least-squares regression model, and the extreme temperature and precipitation were analyzed using hydrological statistical methods. The results show the following： the mean annual temperature increased significantly from 1950 to 2010 in the five regions, mainly due to the warming in spring and winter; the annual precipitation changed significantly from 1950 to 2010 only in the lower mainstream of the Amur River; the frequency of extremely low temperature events decreased from 1953 to 1995 in the mainstream of the Amur River; the frequency of high temperature events increased from 1953 to 1995 in the mainstream of the Amur River; and the frequency of extreme precipitation events did not change significantly from 1953 to 1995 in the mainstream of the Amur River. This study provides a valuable theoretical basis for settling disputes between China and Russia on sustainable development and utilization of water resources of the Amur River.

Isolation and Identification of a High-pathogenicity Strepto- coccus suis Serotype 7 Strain

In order to diagnose the diseased pigs in a certain large pig farm in Binzhou City, Shandong Province, the dead piglets with joint swelling were subjected to necroscopy, and the pathogenic bacterium was isolated and identified. One Gram-positive Streptococcus was isolated. The strain was subjected to characteristic culture, microscopic examination and molecular biological identification, and resistance detection, animal regression experiment and mouse pathogenicity test were carried out. The results showed that the isolate was identified to be Streptococcus suis serotype 7, which was resistant to multiple drugs; and the pathogenicity test showed that the strain had high pathogenicity to pigs, resulting in neurosis on partial pigs, and the strain had no pathogenicity to Kunming and BALB/c mice but certain pathogenicity to CD1 mice.

Analysis on the Correlation between Stock Exchange and Economic Growth

The problem considered is the correlation between stock exchange and economy growth. Stepwise regression is being used on the following figures： increasing rate of GDP, the volume of stock market, and liquidity. As a result, we give .an.equation of national economy and stock market. Then, we use Granger＇s Causality test to prove that the stock market has positive effects on the national economy.

Survival Rate Analysis on Breast Cancer Cases at Univesity College Hospital, Ibadan, Nigeria

Breast cancer is one of the leading diseases that affect women’s lives. It affects their lives in so many ways by denying them the required standard of health needed to carry out all of their daily activities for some days, weeks, months or years before eventually causing death. This research estimates the survival rate of breast cancer patients and investigates the effects of stage of tumor, gender, age, ethnic group, occupation, marital status and type of cancer upon the survival of patients. Data used for the study were extracted from the case file of patients in the Radiation Oncology Department, University College Hospital, Ibadan using a well-structured pro forma in which 74 observations were censored and 30 events occurred. The Kaplan-Meier estimator was used to estimate the overall survival probability of breast cancer patients following their recruitment into the study and determine the mean and median survival times of breast cancer patients following their time of recruitment into the study. Since there are different groups with respect to the stages of tumor at the time of diagnosis, the log-rank test was used to compare the survival curve of the stages of tumor with considering p-values below 0.05 as statistically significant. Multivariate Cox regression was used to investigate the effects of some variables on the survival of patients. The overall cumulative survival probability obtained is 0.175 (17.5%). The overall estimated mean time until death is 28.751 weeks while the median time between admission and death is 23 weeks. As the p-value (0.000032) of the log-rank test for comparing stages of tumor is less than 0.05, it is concluded that there is significant evidence of a difference in survival times for the stages of tumor. The survival function plot for the stages of tumor shows that patients with stage III tumor are less likely to survive. From the estimated mean time until death for the stages of tumor, it was deduced that stage I tumor patients have an increased chance of survival. Types of cancer, gender, marital status, ethnic group, occupation and patient’s age at entry into the study are not important predictors of chances of survival.

DeltaFuzz: Historical Version Information Guided Fuzz Testing

With the widespread use of agile software development methods,such as agile and scrum,software is iteratively updated more frequently.To ensure the quality of the software,regression testing is conducted before new versions are released.Moreover,to improve the efficiency of regression testing,testing efforts should be concentrated on the modified and impacted parts of a program.However,the costs of manually constructing new test cases for the modified and impacted parts are relatively expensive.Fuzz testing is an effective method for generating test data automatically,but it is usually devoted to achieving higher code coverage,which makes fuzz testing unsuitable for direct regression testing scenarios.For this reason,we propose a fuzz testing method based on the guidance of historical version information.First,the differences between the program being tested and the last version are analyzed,and the results of the analysis are used to locate change points.Second,change impact analysis is performed to find the corresponding impacted basic blocks.Finally,the fitness values of test cases are calculated according to the execution traces,and new test cases are generated iteratively by the genetic algorithm.Based on the proposed method,we implement a prototype tool DeltaFuzz and conduct experiments on six open-source projects.Compared with the fuzzing tool AFLGo,AFLFast and AFL,DeltaFuzz can reach the target faster,and the time taken by DeltaFuzz was reduced by 20.59%,30.05%and 32.61%,respectively.

Robust U-type test for high dimensional regression coefficients using refitted cross-validation variance estimation

This paper aims to develop a new robust U-type test for high dimensional regression coefficients using the estimated U-statistic of order two and refitted cross-validation error variance estimation. It is proved that the limiting null distribution of the proposed new test is normal under two kinds of ordinary models.We further study the local power of the proposed test and compare with other competitive tests for high dimensional data. The idea of refitted cross-validation approach is utilized to reduce the bias of sample variance in the estimation of the test statistic. Our theoretical results indicate that the proposed test can have even more substantial power gain than the test by Zhong and Chen(2011) when testing a hypothesis with outlying observations and heavy tailed distributions. We assess the finite-sample performance of the proposed test by examining its size and power via Monte Carlo studies. We also illustrate the application of the proposed test by an empirical analysis of a real data example.

DIMENSION-REDUCTION TYPE TESTFOR LINEARITY OF ASTOCHASTIC REGRESSION MODEL

This article investigates the test for linearity of a multivariate stochastic regression model.The use of nonparametric regression procedures for developing regression diagnostics has beenthe subject of several recent research efforts. However, when the dimension of the regressor islarge, some traditional nonparametric methods, such as kernel estimation, may be inefficient.We in this article suggest two test statistics based on projection pursuit technique and kernelmethod. The tests proposed are consistent against all fixed smooth alternatives to linearityand are asymptotically distribution-free for the distribution of the error. Furthermore, the testsare applied to an example of real-life data and some simulated data sets to demonstrate theavailability of the tests proposed.