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Novel Metrics for Mutation Analysis

Savas Takan1,*, Gokmen Katipoglu2

1 Ankara University, Ankara, 06100, Turkey
2 Kafkas University, Kars, 36100, Turkey

* Corresponding Author: Savas Takan. Email: email

Computer Systems Science and Engineering 2023, 46(2), 2075-2089. https://doi.org/10.32604/csse.2023.036791

Abstract

A measure of the “goodness” or efficiency of the test suite is used to determine the proficiency of a test suite. The appropriateness of the test suite is determined through mutation analysis. Several Finite State Machine (FSM) mutants are produced in mutation analysis by injecting errors against hypotheses. These mutants serve as test subjects for the test suite (TS). The effectiveness of the test suite is proportional to the number of eliminated mutants. The most effective test suite is the one that removes the most significant number of mutants at the optimal time. It is difficult to determine the fault detection ratio of the system. Because it is difficult to identify the system’s potential flaws precisely. In mutation testing, the Fault Detection Ratio (FDR) metric is currently used to express the adequacy of a test suite. However, there are some issues with this metric. If both test suites have the same defect detection rate, the smaller of the two tests is preferred. The test case (TC) is affected by the same issue. The smaller two test cases with identical performance are assumed to have superior performance. Another difficulty involves time. The performance of numerous vehicles claiming to have a perfect mutant capture time is problematic. Our study developed three metrics to address these issues: , , and In this context, most used test generation tools were examined and tested using the developed metrics. Thanks to the metrics we have developed, the research contributes to eliminating the problems related to performance measurement by integrating the missing parameters into the system.

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Cite This Article

APA Style
Takan, S., Katipoglu, G. (2023). Novel metrics for mutation analysis. Computer Systems Science and Engineering, 46(2), 2075-2089. https://doi.org/10.32604/csse.2023.036791
Vancouver Style
Takan S, Katipoglu G. Novel metrics for mutation analysis. Comput Syst Sci Eng. 2023;46(2):2075-2089 https://doi.org/10.32604/csse.2023.036791
IEEE Style
S. Takan and G. Katipoglu, “Novel Metrics for Mutation Analysis,” Comput. Syst. Sci. Eng., vol. 46, no. 2, pp. 2075-2089, 2023. https://doi.org/10.32604/csse.2023.036791



cc Copyright © 2023 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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