Study of Test Cases Prioritization Based on Ant Colony Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2168-2172
Author(s):  
Lu Lu Chen ◽  
Ling Zhang
Keyword(s):  
Ant Colony Algorithm ◽  
Regression Testing ◽  
Time Limit ◽  
Ant Colony ◽  
Test Suite ◽  
Test Cases ◽  
Optimal Sequence ◽  
Effectiveness And Efficiency ◽  
Important Activity

Regression testing is an important activity to ensure the quality of software. In order to improve the efficiency of regression testing, in this paper, the author proposes to reorder test suite based on ant colony algorithm in regression testing, and compare the result with other common sort results. Through experiment, it shows that the method can get the optimal sequence of test cases under the time limit and it has been proven a superior method in both effectiveness and efficiency for test cases prioritization.

Test Suite Minimization in Regression Testing Using Hybrid Approach of ACO and GA

2021 ◽  
pp. 133-150
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Hybrid Approach ◽  
Regression Testing ◽  
Minimum Time ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Ant Colony Optimization Algorithm ◽  
Test Case Selection

This article describes about the application of search-based techniques in regression testing and compares the performance of various search-based techniques for software testing. Test cases tend to increase exponentially as the software is modified. It is essential to remove redundant test cases from the existing test suite. Regression testing is very costly and must be performed in restricted ways to ensure the validity of the existing software. There exist different methods to improve the quality of test cases in terms of the number of faults covered, opposed to the number of statements covered in a minimum time. Different methods exist for this purpose, such as minimization, test case selection, and test case prioritization. In this article, search-based methods are applied to improve the quality of the test suite in order to select a minimum set of test cases which covers all the statements in a minimum time. The whole approach is named search based regression testing. In this paper, the performance of different metaheuristics for test suite minimization problem is also compared with a hybrid approach of ant colony optimization algorithm and genetic algorithm.

Test Suite Minimization in Regression Testing Using Hybrid Approach of ACO and GA

2018 ◽  
Vol 9 (3) ◽  
pp. 88-104
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Hybrid Approach ◽  
Regression Testing ◽  
Minimum Time ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Ant Colony Optimization Algorithm ◽  
Test Case Selection ◽  
Case Selection

This article describes about the application of search-based techniques in regression testing and compares the performance of various search-based techniques for software testing. Test cases tend to increase exponentially as the software is modified. It is essential to remove redundant test cases from the existing test suite. Regression testing is very costly and must be performed in restricted ways to ensure the validity of the existing software. There exist different methods to improve the quality of test cases in terms of the number of faults covered, opposed to the number of statements covered in a minimum time. Different methods exist for this purpose, such as minimization, test case selection, and test case prioritization. In this article, search-based methods are applied to improve the quality of the test suite in order to select a minimum set of test cases which covers all the statements in a minimum time. The whole approach is named search based regression testing. In this paper, the performance of different metaheuristics for test suite minimization problem is also compared with a hybrid approach of ant colony optimization algorithm and genetic algorithm.

scholarly journals Test Case Reduction Using Ant Colony Optimization for Object Oriented Program

2015 ◽  
Vol 5 (6) ◽  
pp. 1424
Author(s):  
Sudhir Kumar Mohapatra ◽  
Srinivas Prasad
Keyword(s):  
Ant Colony Optimization ◽  
System Development ◽  
Object Oriented ◽  
Ant Colony ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Reduction Techniques ◽  
Maintenance Stage ◽  
The Cost

Software testing is one in all the vital stages of system development. In software development, developers continually depend upon testing to reveal bugs. Within the maintenance stage test suite size grow due to integration of new functionalities. Addition of latest technique force to make new test case which increase the cost of test suite. In regression testing new test case could also be added to the test suite throughout the entire testing process. These additions of test cases produce risk of presence of redundant test cases. Because of limitation of time and resource, reduction techniques should be accustomed determine and take away. Analysis shows that a set of the test case in a suit should satisfy all the test objectives that is named as representative set. Redundant test case increase the execution price of the test suite, in spite of NP-completeness of the problem there are few sensible reduction techniques are available. During this paper the previous GA primarily based technique proposed is improved to search out cost optimum representative set using ant colony optimization.

Test Pair Selection for Test Case Prioritization in Regression Testing for WS-BPEL Programs

2013 ◽  
Vol 10 (1) ◽  
pp. 73-102 ◽  
Author(s):  
Lijun Mei ◽  
Yan Cai ◽  
Changjiang Jia ◽  
Bo Jiang ◽  
W.K. Chan
Keyword(s):  
Web Services ◽  
Structural Similarity ◽  
Regression Testing ◽  
Test Pair ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Empirical Results ◽  
Selection For

Many web services not only communicate through XML-based messages, but also may dynamically modify their behaviors by applying different interpretations on XML messages through updating the associated XML Schemas or XML-based interface specifications. Such artifacts are usually complex, allowing XML-based messages conforming to these specifications structurally complex. Testing should cost-effectively cover all scenarios. Test case prioritization is a dimension of regression testing that assures a program from unintended modifications by reordering the test cases within a test suite. However, many existing test case prioritization techniques for regression testing treat test cases of different complexity generically. In this paper, the authors exploit the insights on the structural similarity of XML-based artifacts between test cases in both static and dynamic dimensions, and propose a family of test case prioritization techniques that selects pairs of test case without replacement in turn. To the best of their knowledge, it is the first test case prioritization proposal that selects test case pairs for prioritization. The authors validate their techniques by a suite of benchmarks. The empirical results show that when incorporating all dimensions, some members of our technique family can be more effective than conventional coverage-based techniques.

Optimal Test Case Selection Using Ant Colony and Rough Sets

2020 ◽  
Vol 11 (2) ◽  
pp. 1-14
Author(s):  
Angelin Gladston ◽  
Niranjana Devi N.
Keyword(s):  
Optimization Technique ◽  
Computational Effort ◽  
Ant Colony ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Optimal Test ◽  
Average Percentage ◽  
Test Case Selection ◽  
Case Selection

Test case selection helps in improving quality of test suites by removing ambiguous, redundant test cases, thereby reducing the cost of software testing. Various works carried out have chosen test cases based on single parameter and optimized the test cases using single objective employing single strategies. In this article, a parameter selection technique is combined with an optimization technique for optimizing the selection of test cases. A two-step approach has been employed. In first step, the fuzzy entropy-based filtration is used for test case fitness evaluation and selection. In second step, the improvised ant colony optimization is employed to select test cases from the previously reduced test suite. The experimental evaluation using coverage parameters namely, average percentage statement coverage and average percentage decision coverage along with suite size reduction, demonstrate that by using this proposed approach, test suite size can be reduced, reducing further the computational effort incurred.

Regression Test Reduction for Object-Oriented Software: A Control Call Graph Based Technique and Associated Tool

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Nicolas Frechette ◽  
Linda Badri ◽  
Mourad Badri
Keyword(s):  
Object Oriented ◽  
Control Flow ◽  
Regression Testing ◽  
Test Suite ◽  
Reduced Form ◽  
Test Cases ◽  
Call Graph ◽  
Call Graphs ◽  
Incremental Update ◽  
Flow Graphs

This paper presents a selective regression testing technique and an associated tool for object-oriented software. The technique is based on the concept of Control Call Graphs, which are a reduced form of traditional Control Flow Graphs. It uses static analysis of the source code of the program. The developed tool (1) identifies the Control Call Paths potentially impacted by changes, (2) selects, from an existing test suite, the appropriate test cases, and (3) generates new JUnit test cases for control call paths that are not covered by existing tests (new ones, or those whose structure has been modified after changes). In this way, the approach supports an incremental update of the test suite. The selected JUnit test cases, including the new ones, are automatically executed. Three concrete case studies are reported to provide evidence of the feasibility of the approach and its benefits in terms of reduction of regression testing effort.

Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining

2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Power ◽  
Slow Convergence ◽  
The Impact

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.

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