Empirical Investigation of the Effects of Test Suite Properties on Similarity-Based Test Case Selection

Test suite optimization is an ever-demanded approach for regression test cost reduction. Regression testing is conducted to identify any adverse effects of maintenance activity on previously working versions of the software. It consumes almost seventy percent of the overall software development lifecycle budget. Regression test cost reduction is therefore of vital importance. Test suite optimization is the most explored approach to reduce the test suite size to re-execute. This article focuses on test suite optimization as a regression test case selection, which is a proven N-P hard combinatorial optimization problem. The authors have proposed an almost safe regression test case selection approach using a Hybrid Whale Optimization Algorithm and empirically evaluated the same on subject programs retrieved from the Software Artifact Infrastructure Repository with Bat Search and ACO-based regression test case selection approaches. The analyses of the obtained results indicate an improvement in the fault detection ability of the proposed approach over the compared ones with significant reduction in test suite size.

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Optimal Test Case Selection Using Ant Colony and Rough Sets

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2020040101 ◽

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.

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Exploring Test Suite Diversification and Code Coverage in Multi-Objective Test Case Selection

2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST) ◽

10.1109/icst.2015.7102588 ◽

2015 ◽

Cited By ~ 18

Author(s):

Debajyoti Mondal ◽

Hadi Hemmati ◽

Stephane Durocher

Keyword(s):

Test Suite ◽

Objective Test ◽

Test Case ◽

Code Coverage ◽

Test Case Selection ◽

Multi Objective ◽

Case Selection

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K-Step Crossover Method based on Genetic Algorithm for Test Suite Prioritization in Regression Testing

JUCS - Journal of Universal Computer Science ◽

10.3897/jucs.65241 ◽

2021 ◽

Vol 27 (2) ◽

pp. 170-189

Author(s):

P. K. Gupta

Keyword(s):

Genetic Algorithm ◽

Regression Testing ◽

Test Suite ◽

Test Case ◽

Test Cases ◽

Test Case Prioritization ◽

Average Percentage ◽

Code Coverage ◽

Test Case Selection ◽

Case Selection

Software is an integration of numerous programming modules  (e.g., functions, procedures, legacy system, reusable components, etc.) tested and combined to build the entire module. However, some undesired faults may occur due to a change in modules while performing validation and verification. Retesting of entire software is a costly affair in terms of money and time. Therefore, to avoid retesting of entire software, regression testing is performed. In regression testing, an earlier created test suite is used to retest the software system's modified module. Regression Testing works in three manners; minimizing test cases, selecting test cases, and prioritizing test cases. In this paper, a two-phase algorithm has been proposed that considers test case selection and test case prioritization technique for performing regression testing on several modules ranging from a smaller line of codes to huge line codes of procedural language. A textual based differencing algorithm has been implemented for test case selection. Program statements modified between two modules are used for textual differencing and utilized to identify test cases that affect modified program statements. In the next step, test case prioritization is implemented by applying the Genetic Algorithm for code/condition coverage. Genetic operators: Crossover and Mutation have been applied over the initial population (i.e. test cases), taking code/condition coverage as fitness criterion to provide a prioritized test suite. Prioritization algorithm can be applied over both original and reduced test suite depending upon the test suite's size or the need for accuracy. In the obtained results, the efficiency of the prioritization algorithms has been analyzed by the Average Percentage of Code Coverage (APCC) and Average Percentage of Code Coverage with cost (APCCc). A comparison of the proposed approach is also done with the previously proposed methods and it is observed that APCC & APCCc values achieve higher percentage values faster in the case of the prioritized test suite in contrast to the non-prioritized test suite.

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

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2018070105 ◽

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.

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