Test case prioritization techniques “an empirical study”

We propose a test case prioritization strategy for risk based testing, in which the risk exposure is employed as the key criterion of evaluation. Existing approaches to risk based testing typically employ risk exposure values as assessed by the tester. In contrast, we employ exposure values that have been determined by experts during the risk assessment stage of the risk management process. If a given method produces greater accuracy in fault detection, that approach is considered more valuable for software testing. We demonstrate the value of our proposed risk based testing method in this sense through its application.

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Building Ant System for Multi-Faceted Test Case Prioritization

10.4018/978-1-6684-3702-5.ch034 ◽

2022 ◽

pp. 671-686

Author(s):

Manoj Kumar Pachariya

Keyword(s):

Experimental Study ◽

Empirical Study ◽

Execution Time ◽

Optimal Solution ◽

Time Constraints ◽

Test Case ◽

Test Cases ◽

Test Case Prioritization ◽

Ant System ◽

Software Infrastructure

This article presents the empirical study of multi-criteria test case prioritization. In this article, a test case prioritization problem with time constraints is being solved by using the ant colony optimization (ACO) approach. The ACO is a meta-heuristic and nature-inspired approach that has been applied for the statement of a coverage-based test case prioritization problem. The proposed approach ranks test cases using statement coverage as a fitness criteria and the execution time as a constraint. The proposed approach is implemented in MatLab and validated on widely used benchmark dataset, freely available on the Software Infrastructure Repository (SIR). The results of experimental study show that the proposed ACO based approach provides near optimal solution to test case prioritization problem.

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Building Ant System for Multi-Faceted Test Case Prioritization

International Journal of Software Innovation ◽

10.4018/ijsi.2020040102 ◽

2020 ◽

Vol 8 (2) ◽

pp. 23-37

Author(s):

Manoj Kumar Pachariya

Keyword(s):

Experimental Study ◽

Empirical Study ◽

Execution Time ◽

Optimal Solution ◽

Time Constraints ◽

Test Case ◽

Test Cases ◽

Test Case Prioritization ◽

Ant System ◽

Software Infrastructure

This article presents the empirical study of multi-criteria test case prioritization. In this article, a test case prioritization problem with time constraints is being solved by using the ant colony optimization (ACO) approach. The ACO is a meta-heuristic and nature-inspired approach that has been applied for the statement of a coverage-based test case prioritization problem. The proposed approach ranks test cases using statement coverage as a fitness criteria and the execution time as a constraint. The proposed approach is implemented in MatLab and validated on widely used benchmark dataset, freely available on the Software Infrastructure Repository (SIR). The results of experimental study show that the proposed ACO based approach provides near optimal solution to test case prioritization problem.

Download Full-text