Optimization of Mutation Testing Challenges to Fixing Faults
How to cite (IJASEIT) :
A. Aghamohammadi, S. H. Mirian-Hosseinabadi, and S. Jalali, “Statement frequency coverage: A code coverage criterion for assessing test suite effectiveness,” Inf. Softw. Technol., vol. 129, no. September 2020, p. 106426, 2021.
A. Mustafa, W. M. N. Wan-Kadir, and N. Ibrahim, “Comparative evaluation of the state-of-art requirements-based test case generation approaches,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 4-2 Special Issue, pp. 1567-1573, 2017.
F. F. Ismail, R. Razali, and Z. Mansor, “Considerations for cost estimation of software testing outsourcing projects,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 1, pp. 142-152, 2019.
P. Delgado-Pí©rez and F. Chicano, “An experimental and practical study on the equivalent mutant connection: An evolutionary approach,” Inf. Softw. Technol., vol. 124, no. April, 2020.
X. Dang, X. Yao, D. Gong, T. Tian, and B. Sun, “Multi-Task Optimization-Based Test Data Generation for Mutation Testing via Relevance of Mutant Branch and Input Variable,” IEEE Access, vol. 8, pp. 144401-144412, 2020.
P. Pinheiro et al., “Mutating code annotations: An empirical evaluation on Java and C# programs,” Sci. Comput. Program., vol. 191, p. 102418, 2020.
N. Gupta, A. Sharma, and M. K. Pachariya, “Multi-objective test suite optimization for detection and localization of software faults,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, 2020.
A. Usman, N. Ibrahim, and I. A. Salihu, “TEGDroid: Test case generation approach for android apps considering context and GUI events,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 1, pp. 16-23, 2020.
S. A. Arnomo and N. Binti Ibrahim, “Priority path for mutant repairs on mutation testing,” Proc. ICAITI 2019 - 2nd Int. Conf. Appl. Inf. Technol. Innov. Explor. Futur. Technol. Appl. Inf. Technol. Innov., pp. 71-76, 2019.
J. A. do Prado Lima and S. R. Vergilio, “A systematic mapping study on higher order mutation testing,” J. Syst. Softw., vol. 154, pp. 92-109, 2019.
A. V. Pizzoleto, F. C. Ferrari, J. Offutt, L. Fernandes, and M. Ribeiro, “A systematic literature review of techniques and metrics to reduce the cost of mutation testing,” J. Syst. Softw., vol. 157, p. 110388, 2019.
A. M. Kazerouni, J. C. Davis, A. Basak, C. A. Shaffer, F. Servant, and S. H. Edwards, “Fast and accurate incremental feedback for students’ software tests using selective mutation analysis,” J. Syst. Softw., vol. 175, p. 110905, 2021.
X. Yao, G. Zhang, F. Pan, D. Gong, and C. Wei, “Orderly Generation of Test Data via Sorting Mutant Branches Based on Their Dominance Degrees for Weak Mutation Testing,” IEEE Trans. Softw. Eng., vol. 5589, no. c, pp. 1-17, 2020.
R. Gheyi et al., “Identifying method-level mutation subsumption relations using Z3,” Inf. Softw. Technol., vol. 132, no. April 2020, p. 106496, 2021.
L. Villalobos-Arias, C. Quesada-López, A. Martínez, and M. Jenkins, “Evaluation of a model-based testing platform for Java applications,” IET Softw., vol. 14, no. 2, pp. 115-128, 2020.
H. Wang, B. Du, J. He, Y. Liu, and X. Chen, “IETCR: An Information Entropy Based Test Case Reduction Strategy for Mutation-Based Fault Localization,” IEEE Access, vol. 8, pp. 124297-124310, 2020.
J. M. Zhang, L. Zhang, D. Hao, L. Zhang, and M. Harman, “An empirical comparison of mutant selection assessment metrics,” Proc. - 2019 IEEE 12th Int. Conf. Softw. Testing, Verif. Valid. Work. ICSTW 2019, pp. 90-101, 2019.
L. Gutierrez-Madronal, A. Garcia-Dominguez, and I. Medina-Bulo, “Combining Evolutionary Mutation Testing with Random Selection,” 2020 IEEE Congr. Evol. Comput. CEC 2020 - Conf. Proc., 2020.
M. B. Bashir and A. Nadeem, “Improved Genetic Algorithm to Reduce Mutation Testing Cost,” IEEE Access, vol. 5, no. c, pp. 3657-3674, 2017.
N. Jatana and B. Suri, “Particle Swarm and Genetic Algorithm applied to mutation testing for test data generation: A comparative evaluation,” J. King Saud Univ. - Comput. Inf. Sci., vol. 32, no. 4, pp. 514-521, 2020.
M. Nosrati, H. Haghighi, and M. Vahidi Asl, “Test data generation using genetic programming,” Inf. Softw. Technol., vol. 130, no. September, p. 106446, 2021.
R. Jangra and R. Kait, “Analysis and comparison among Ant System; Ant Colony System and Max-Min Ant System with different parameters setting,” 3rd IEEE Int. Conf. , pp. 1-4, 2017.
D. N. Mudaliar and N. K. Modi, “Design and Application of m-Mutation Operator in Genetic Algorithm to Solve Traveling Salesman Problem,” 8th Int. Conf. Comput. Power, Energy, Inf. Commun. ICCPEIC 2019, pp. 94-96, 2019.
Q. Zhu, A. Zaidman, and A. Panichella, “How to kill them all: An exploratory study on the impact of code observability on mutation testing,” J. Syst. Softw., vol. 173, p. 110864, 2021.
Z. X. Lu, S. Vercammen, and S. Demeyer, “Semi-Automatic Test Case Expansion for Mutation Testing,” VST 2020 - Proc. 2020 IEEE 3rd Int. Work. Validation, Anal. Evol. Softw. Tests, pp. 1-7, 2020.
N. Yang and Y. Shi, “Research on Tourist Route based on a Novel Ant Colony Optimization Algorithm,” 2019 IEEE Int. Conf. Power, Intell. Comput. Syst. ICPICS 2019, no. 3, pp. 160-163, 2019.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).