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.

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.

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.

Novel technique for cost reduction in mutation testing

Aimed at the problem of expensive costs in mutation testing which has hampered its wide use,a technique of introducing a test case selection into the process of mutation testing is proposed.For each mutant,a fixed number of test cases are selected to constrain the maximum allowable executions so as to reduce useless work.Test case selection largely depends on the degree of mutation.The mutation distance is an index describing the semantic difference between the original program and the mutated program.It represents the percentage of effective test cases in a test set,so it can be used to guide the selection of test cases.The bigger the mutation distance is,the easier it is that the mutant will be killed,so the corresponding number of effective test cases for this mutant is greater.Experimental results suggest that the technique can remarkably reduce execution costs without a significant loss of test effectiveness.

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.

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 challeng- ing task during software testing. Among techniques studied in this field, program spectrum based fault localization is a promising approach. To perform spectrum based fault local- ization, a set of test oracles should be provided, and the ef- fectiveness of fault localization depends highly on the quality of test oracles. Moreover, their effectiveness is usually af- fected 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 fauk localization process, i.e., an iterative process of selecting test cases for effective fault localization （IPSETFUL）, to identify as many faults as pos- sible in the program until the stopping criterion is satisfied. It is performed based on a concept lattice of program spec- trum （CLPS） proposed in our previous work. Based on the labeling approach of CLPS, program statements are catego- rized as dangerous statements, safe statements, and sensitive statements. To identify the faults, developers need to check the dangerous statements. Meantime, developers need to se- lect 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 pro- ceeded 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 identify most 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.