research-article

Test Data Generation for Path Coverage of MPI Programs Using SAEO

Authors:

Tian TianAuthors Info & Claims

ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 30, Issue 2

Article No.: 17, Pages 1 - 37

https://doi.org/10.1145/3423132

Published: 03 January 2021 Publication History

Abstract

Message-passing interface (MPI) programs, a typical kind of parallel programs, have been commonly used in various applications. However, it generally takes exhaustive computation to run these programs when generating test data to test them. In this article, we propose a method of test data generation for path coverage of MPI programs using surrogate-assisted evolutionary optimization, which can efficiently generate test data with high quality. We first divide a sample set of a program into a number of clusters according to the multi-mode characteristic of the coverage problem, with each cluster training a surrogate model. Then, we estimate the fitness of each individual using one or more surrogate models when generating test data through evolving a population. Finally, a small number of representative individuals are selected to execute the program, with the purpose of obtaining their real fitness, to guide the subsequent evolution of the population. We apply the proposed method to seven benchmark MPI programs and compare it with several state-of-the-art approaches. The experimental results show that the proposed method can generate test data with reduced computation, thus improving the testing efficiency.

References

[1]

Artur Andrzejak and Thomas Bach. 2018. Practical amplification of condition/decision test coverage by combinatorial testing. In Proceedings of the IEEE International Conference on Software Testing, Verification, and Validation Workshops (ICSTW’18). IEEE, 341--347.

[2]

Muhammad Waqar Aziz and Syed Abdul Baqi Shah. 2015. Test-data generation for testing parallel real-time systems. In Proceedings of the IFIP International Conference on Testing Software and Systems. Springer, 211--223.

Digital Library

[3]

Muhammad Waqar Aziz and Syed Abdul Baqi Shah. 2019. Evolutionary testing for timing analysis of parallel embedded software. Int. Arab J. Info. Technol. 16, 3 (2019), 415--423.

[4]

Michel Berkelaar, Kjell Eikland, Peter Notebaert, et al. 2004. Lpsolve: Open source (mixed-integer) linear programming system. Eindhoven U. Technol. 63 (2004).

[5]

Antonia Bertolino and Martina Marré. 1994. Automatic generation of path covers based on the control flow analysis of computer programs. IEEE Trans. Software Eng. 20, 12 (1994), 885--899.

Digital Library

[6]

Subhodip Biswas, Souvik Kundu, and Swagatam Das. 2014. Inducing niching behavior in differential evolution through local information sharing. IEEE Trans. Evolution. Comput. 19, 2 (2014), 246--263.

Digital Library

[7]

Jeff Boyle. 1994. An application of Fourier series to the most significant digit problem. Amer. Math. Monthly 101, 9 (1994), 879--886.

[8]

Stefan Bucur, Vlad Ureche, Cristian Zamfir, and George Candea. 2011. Parallel symbolic execution for automated real-world software testing. In Proceedings of the 6th Conference on Computer Systems. 183--198.

Digital Library

[9]

Cristian Cadar, Daniel Dunbar, Dawson R. Engler, et al. 2008. KLEE: Unassisted and automatic generation of high-coverage tests for complex systems programs. In Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI’08), Vol. 8. 209--224.

[10]

Yulian Cao, Han Zhang, Wenfeng Li, Mengchu Zhou, Yu Zhang, and Wanpracha Art Chaovalitwongse. 2019. Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions. IEEE Trans. Evolution. Comput. 23, 4 (2019), 719--731.

[11]

Guoliang Chen, Hong An, Ling Chen, Q. Zheng, and J. L. Shan. 2004. Parallel Algorithm Practice. Higher Education Press, Beijing, 353--355.

[12]

Zong-Gan Chen, Zhi-Hui Zhan, Hua Wang, and Jun Zhang. 2020. Distributed individuals for multiple peaks: A novel differential evolution for multimodal optimization problems. IEEE Trans. Evolution. Comput. 24, 4 (2020), 708--719.

[13]

Tinkle Chugh, Yaochu Jin, Kaisa Miettinen, Jussi Hakanen, and Karthik Sindhya. 2016. A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization. IEEE Trans. Evolution. Comput. 22, 1 (2016), 129--142.

[14]

Silvia Margarita Diaz Diaz. 2019. Structural Testing Criteria for Concurrent Programs Considering Loop Execution. Ph.D. Dissertation. Universidade de São Paulo.

[15]

Silvia Margarita Diaz Diaz, Paulo Lopes Souza, and Simone Souza. 2019. A structural testing tool for MPI programs with loops. In Proceedings of the Anais do XX Simpósio em Sistemas Computacionais de Alto Desempenho. SBC, 406--417.

[16]

Xianjin Fu, Zhenbang Chen, Yufeng Zhang, Chun Huang, Wei Dong, and Ji Wang. 2015. MPISE: Symbolic execution of MPI programs. In Proceedings of the IEEE 16th International Symposium on High Assurance Systems Engineering. IEEE, 181--188.

Digital Library

[17]

Salvador García, Julián Luengo, and Francisco Herrera. 2015. Data Preprocessing in Data Mining. Springer.

Digital Library

[18]

Dunwei Gong, Chen Zhang, Tian Tian, and Zheng Li. 2016. Reducing scheduling sequences of message-passing parallel programs. Info. Softw. Technol. 80 (2016), 217--230.

Digital Library

[19]

William Gropp. 2002. MPICH2: A new start for MPI implementations. In Proceedings of the European Parallel Virtual Machine/Message Passing Interface Users Group Meeting. Springer, 7--7.

[20]

William Gropp, Ewing Lusk, Nathan Doss, and Anthony Skjellum. 1996. A high-performance, portable implementation of the MPI message passing interface standard. Parallel Comput. 22, 6 (1996), 789--828.

Digital Library

[21]

Dan Guo, Yaochu Jin, Jinliang Ding, and Tianyou Chai. 2018. Heterogeneous ensemble-based infill criterion for evolutionary multiobjective optimization of expensive problems. IEEE Trans. Cybernet. 49, 3 (2018), 1012--1025.

[22]

Alexandre Ceolin Hausen, Silvia Regina Vergilio, Simone do Rocio Senger de Souza, Paulo Sergio Lopes de Souza, and Adenilso da Silva Simão. 2007. A tool for structural testing of MPI programs. In Proceedings of the 8th IEEE Latin-American Test Workshop (LATW’07). 1--6.

[23]

Ting Huang, Yue-Jiao Gong, Sam Kwong, Hua Wang, and Jun Zhang. 2020. A niching memetic algorithm for multi-solution traveling salesman problem. IEEE Trans. Evolution. Comput. 24, 3 (2020), 508--522.

[24]

Sheldon Hui and Ponnuthurai N. Suganthan. 2015. Ensemble and arithmetic recombination-based speciation differential evolution for multimodal optimization. IEEE Trans. Cybernet. 46, 1 (2015), 64--74.

[25]

Christian Igel, Verena Heidrich-Meisner, and Tobias Glasmachers. 2008. Shark. J. Mach. Learn. Res. 9 (2008), 993--996.

Digital Library

[26]

Neetu Jain and Rabins Porwal. 2019. Automated test data generation applying heuristic approaches a survey. In Software Engineering. Springer, 699--708.

[27]

Yaochu Jin. 2011. Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm Evolution. Comput. 1, 2 (2011), 61--70.

[28]

Yaochu Jin and Bernhard Sendhoff. 2004. Reducing fitness evaluations using clustering techniques and neural network ensembles. In Genetic and Evolutionary Computation Conference. Springer, 688--699.

[29]

Shivam Kalra, Shahryar Rahnamayan, and Kalyanmoy Deb. 2017. Enhancing clearing-based niching method using Delaunay Triangulation. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC’17). IEEE, 2328--2337.

[30]

Bohuslav Křena, Zdeněk Letko, Tomáš Vojnar, and Shmuel Ur. 2010. A platform for search-based testing of concurrent software. In Proceedings of the 8th Workshop on Parallel and Distributed Systems: Testing, Analysis, and Debugging. 48--58.

Digital Library

[31]

Eric Lantz. 2008. Using microsoft message passing interface (MS MPI). Windows HPC Server (2008), 48.

[32]

Genghui Li, Qingfu Zhang, Jianyong Sun, and Zhonghua Han. 2019. Radial basis function assisted optimization method with batch infill sampling criterion for expensive optimization. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC’19). IEEE, 1664--1671.

[33]

Hongbo Li, Zizhong Chen, and Rajiv Gupta. 2019. Efficient concolic testing of MPI applications. In Proceedings of the 28th International Conference on Compiler Construction. 193--204.

Digital Library

[34]

Hongbo Li, Sihuan Li, Zachary Benavides, Zizhong Chen, and Rajiv Gupta. 2018. COMPI: Concolic testing for MPI applications. In Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS’18). IEEE, 865--874.

[35]

Xiaodong Li. 2007. A multimodal particle swarm optimizer based on fitness Euclidean-distance ratio. In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation. ACM, 78--85.

Digital Library

[36]

Xiaodong Li, Michael G. Epitropakis, Kalyanmoy Deb, and Andries Engelbrecht. 2016. Seeking multiple solutions: An updated survey on niching methods and their applications. IEEE Trans. Evolution. Comput. 21, 4 (2016), 518--538.

Digital Library

[37]

Yong Liu, Wang Chen, Jianjun Hu, Xiaojun Zheng, and Yanjun Shi. 2017. Ensemble of surrogates with an evolutionary multi-agent system. In Proceedings of the IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD’17). IEEE, 521--525.

[38]

Chengyu Lu, Jinghui Zhong, Yinxing Xue, Liang Feng, and Jun Zhang. 2020. Ant colony system with sorting-based local search for coverage-based test case prioritization. IEEE Trans. Reliabil. 69, 3 (2020), 1004--1020.

[39]

Jianping Luo, Abhishek Gupta, Yew-Soon Ong, and Zhenkun Wang. 2018. Evolutionary optimization of expensive multiobjective problems with co-sub-Pareto front Gaussian process surrogates. IEEE Trans. Cybernet. 49, 5 (2018), 1708--1721.

[40]

Wenjian Luo, Ruikang Yi, Bin Yang, and Peilan Xu. 2018. Surrogate-assisted evolutionary framework for data-driven dynamic optimization. IEEE Trans. Emerg. Topics Comput. Intell. 3, 2 (2018), 137--150.

[41]

Linhai Ma, Peng Wu, and Tsong Yueh Chen. 2018. Diversity driven adaptive test generation for concurrent data structures. Info. Softw. Technol. 103 (2018), 162--173.

[42]

Thomas J. McCabe. 1976. A complexity measure. IEEE Trans. Softw. Eng.4 (1976), 308--320.

Digital Library

[43]

Silvana M. Melo, Jeffrey C. Carver, Paulo Sergio Lopes de Souza, and Simone do Rocio Senger de Souza. 2019. Empirical research on concurrent software testing: A systematic mapping study. Info. Softw. Technol. 105 (2019), 226--251.

[44]

Matthias Müller, Bronis de Supinski, Ganesh Gopalakrishnan, Tobias Hilbrich, and David Lecomber. 2011. Dealing with MPI bugs at scale: Best practices, automatic detection, debugging, and formal verification. Slides Presented in this Tutorial, Integrating Presentations from Dresden, Allinea, LLNL, and Utah. Retrieved from http://www.cs.utah.edu/fv/publications/sc11_with_handson.pptx.

[45]

M. Munlin and M. Anantathanavit. 2017. New social-based radius particle swarm optimization. In Proceedings of the 12th IEEE Conference on Industrial Electronics and Applications (ICIEA’17). IEEE, 838--843.

[46]

Glenford J. Myers, Tom Badgett, Todd M. Thomas, and Corey Sandler. 2004. The Art of Software Testing. Vol. 2. Wiley Online Library.

Digital Library

[47]

Edgar Covantes Osuna and Dirk Sudholt. 2020. Runtime analysis of crowding mechanisms for multimodal optimisation. IEEE Trans. Evolution. Comput. 24, 3 (2020), 581--592.

[48]

Annibale Panichella, Fitsum Meshesha Kifetew, and Paolo Tonella. 2017. Automated test case generation as a many-objective optimisation problem with dynamic selection of the targets. IEEE Trans. Softw. Eng. 44, 2 (2017), 122--158.

[49]

Roy P. Pargas, Mary Jean Harrold, and Robert R. Peck. 1999. Test-data generation using genetic algorithms. Softw. Test. Verificat. Reliabil. 9, 4 (1999), 263--282.

[50]

Konstantinos E. Parsopoulos and Michael N. Vrahatis. 2004. On the computation of all global minimizers through particle swarm optimization. IEEE Trans. Evolution. Comput. 8, 3 (2004), 211--224.

Digital Library

[51]

Ivy Bo Peng, Stefano Markidis, Roberto Gioiosa, Gokcen Kestor, and Erwin Laure. 2017. MPI streams for hpc applications. New Front. High Perform. Comput. Big Data 30 (2017), 75.

[52]

Bo-Yang Qu and Ponnuthurai Nagaratnam Suganthan. 2010. Novel multimodal problems and differential evolution with ensemble of restricted tournament selection. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE, 1--7.

[53]

Bo-Yang Qu, Ponnuthurai Nagaratnam Suganthan, and Swagatam Das. 2012. A distance-based locally informed particle swarm model for multimodal optimization. IEEE Trans. Evolution. Comput. 17, 3 (2012), 387--402.

Digital Library

[54]

Bo-Yang Qu, Ponnuthurai Nagaratnam Suganthan, and Jane-Jing Liang. 2012. Differential evolution with neighborhood mutation for multimodal optimization. IEEE Trans. Evolution. Comput. 16, 5 (2012), 601--614.

Digital Library

[55]

José Miguel Rojas, Mattia Vivanti, Andrea Arcuri, and Gordon Fraser. 2017. A detailed investigation of the effectiveness of whole test suite generation. Empir. Softw. Eng. 22, 2 (2017), 852--893.

Digital Library

[56]

Thomas Sewell, Felix Kam, and Gernot Heiser. 2016. Complete, high-assurance determination of loop bounds and infeasible paths for WCET analysis. In Proceedings of the IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS’16). IEEE, 1--11.

[57]

Hossein Sharifipour, Mojtaba Shakeri, and Hassan Haghighi. 2018. Structural test data generation using a memetic ant colony optimization based on evolution strategies. Swarm Evolution. Comput. 40 (2018), 76--91.

[58]

Paulo Sérgio Lopes de Souza, Simne do Rocio Senger de Souza, and Ed Zaluska. 2014. Structural testing for message-passing concurrent programs: an extended test model. Concurr. Comput.: Pract. Exper. 26, 1 (2014), 21--50.

[59]

Simone do Rocio Senger de Souza, Silvia Regina Vergilio, Paulo Sérgio Lopes de Souza, Adenilso da Silva Simão, and Alexandre Ceolin Hausen. 2008. Structural testing criteria for message-passing parallel programs. Concurr. Comput.: Pract. Exper. 20, 16 (2008), 1893--1916.

[60]

Baicai Sun, Dunwei Gong, Tian Tian, and Xiangjuan Yao. 2020. Integrating an ensemble surrogate model’s estimation into test data generation. IEEE Trans. Softw. Eng. (2020).

[61]

Baicai Sun, Jinxin Wang, Dunwei Gong, and Tian Tian. 2019. Scheduling sequence selection for generating test data to cover paths of MPI programs. Info. Softw. Technol. 114 (2019), 190--203.

[62]

Chaoli Sun, Yaochu Jin, Jianchao Zeng, and Yang Yu. 2015. A two-layer surrogate-assisted particle swarm optimization algorithm. Soft Comput. 19, 6 (2015), 1461--1475.

Digital Library

[63]

Alexander Valerievich Supalov, Michael Vyacheslavovich Chuvelev, Dmitriy Vitalievich Dontsov, and Vladimir Dmitrievich Truschin. 2016. Message passing interface tuning using collective operation modeling. U.S. Patent 9,448,863.

[64]

Tian Tian and Dunwei Gong. 2016. Test data generation for path coverage of message-passing parallel programs based on co-evolutionary genetic algorithms. Autom. Softw. Eng. 23, 3 (2016), 469--500.

Digital Library

[65]

Tian Tian, Dunwei Gong, Fei-Ching Kuo, and Huai Liu. 2019. Genetic algorithm based test data generation for MPI parallel programs with blocking communication. J. Syst. Softw. 155 (2019), 130--144.

Digital Library

[66]

Hao Tong, Changwu Huang, Jialin Liu, and Xin Yao. 2019. Voronoi-based efficient surrogate-assisted evolutionary algorithm for very expensive problems. Retrieved from https://Arxiv:1901.05755.

[67]

Jeffrey S. Vetter and Bronis R. De Supinski. 2000. Dynamic software testing of MPI applications with umpire. In Proceedings of the ACM/IEEE Conference on Supercomputing (SC’00). IEEE, 51--51.

[68]

Ricardo F. Vilela, Victor H. S. C. Pinto, Thelma E. Colanzi, and Simone do Rocio Senger de Souza. 2019. Bio-inspired optimization of test data generation for concurrent software. In Proceedings of the International Symposium on Search Based Software Engineering. Springer, 121--136.

[69]

Handing Wang, Yaochu Jin, Chaoli Sun, and John Doherty. 2018. Offline data-driven evolutionary optimization using selective surrogate ensembles. IEEE Trans. Evolution. Comput. 23, 2 (2018), 203--216.

[70]

Zi-Jia Wang, Zhi-Hui Zhan, Ying Lin, Wei-Jie Yu, Hua Wang, Sam Kwong, and Jun Zhang. 2020. Automatic niching differential evolution with contour prediction approach for multimodal optimization problems. IEEE Trans. Evolution. Comput. 24, 1 (2020), 114--128.

Digital Library

[71]

Andreas Windisch, Stefan Wappler, and Joachim Wegener. 2007. Applying particle swarm optimization to software testing. In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation. ACM, 1121--1128.

Digital Library

[72]

Peng Xiao, Bin Liu, and Shihai Wang. 2018. Feedback-based integrated prediction: Defect prediction based on feedback from software testing process. J. Syst. Softw. 143 (2018), 159--171.

[73]

Zhongxing Xu and Jian Zhang. 2006. A test data generation tool for unit testing of C programs. In Proceedings of the 6th International Conference on Quality Software (QSIC’06). IEEE, 107--116.

Digital Library

[74]

Jun Yan and Jian Zhang. 2008. An efficient method to generate feasible paths for basis path testing. Info. Process. Lett. 107, 3–4 (2008), 87--92.

Digital Library

[75]

Cheer-Sun D. Yang, Amie L. Souter, and Lori L. Pollock. 1998. All-du-path coverage for parallel programs. ACM SIGSOFT Softw. Eng. Notes 23, 2 (1998), 153--162.

Digital Library

[76]

Hengbiao Yu. 2018. Combining symbolic execution and model checking to verify MPI programs. In Proceedings of the 40th International Conference on Software Engineering. 527--530.

Digital Library

[77]

Haibo Yu, Ying Tan, Jianchao Zeng, Chaoli Sun, and Yaochu Jin. 2018. Surrogate-assisted hierarchical particle swarm optimization. Info. Sci. 454 (2018), 59--72.

[78]

Caitong Yue, Boyang Qu, and Jing Liang. 2017. A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems. IEEE Trans. Evolution. Comput. 22, 5 (2017), 805--817.

[79]

Li Yujian and Liu Bo. 2007. A normalized levenshtein distance metric. IEEE Trans. Pattern Anal. Mach. Intell. 29, 6 (2007), 1091--1095.

Digital Library

[80]

Saúl Zapotecas Martínez and Carlos A. Coello Coello. 2013. MOEA/D assisted by RBF networks for expensive multi-objective optimization problems. In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation. ACM, 1405--1412.

[81]

Hua Zhang and Xiangbing Zhou. 2018. A novel clustering algorithm combining niche genetic algorithm with canopy and K-means. In Proceedings of the International Conference on Artificial Intelligence and Big Data (ICAIBD’18). IEEE, 26--32.

[82]

Yu-Hui Zhang, Yue-Jiao Gong, Ying Gao, Hua Wang, and Jun Zhang. 2020. Parameter-free Voronoi neighborhood for evolutionary multimodal optimization. IEEE Trans. Evolution. Comput. 24, 2 (2020), 335--349.

[83]

Yu-Hui Zhang, Yue-Jiao Gong, Hua-Xiang Zhang, Tian-Long Gu, and Jun Zhang. 2016. Toward fast niching evolutionary algorithms: A locality sensitive hashing-based approach. IEEE Trans. Evolution. Comput. 21, 3 (2016), 347--362.

[84]

Rong Zhou and Eric A. Hansen. 2006. Breadth-first heuristic search. Artific. Intell. 170, 4–5 (2006), 385--408.

Cited By

Liu FHuang HXiang YRoychoudhury APaiva AAbreu RStorey M(2024)Micro-scale Concolic Testing Framework for Automated Test Data Generation Based on Path CoverageProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings10.1145/3639478.3643067(284-285)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639478.3643067
Wang YCui WWang GWang JGong D(2024)Improving Test Data Generation for MPI Program Path Coverage With FERPSO-IMPR and Surrogate-Assisted ModelsIEEE Transactions on Software Engineering10.1109/TSE.2024.335497150:3(495-511)Online publication date: Mar-2024
https://doi.org/10.1109/TSE.2024.3354971
Dang XWang JGong DYao XWei CXu B(2024)Test data generation for covering mutation-based path using MGA for MPI programJournal of Systems and Software10.1016/j.jss.2024.111962210:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.jss.2024.111962
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Index Terms

Test Data Generation for Path Coverage of MPI Programs Using SAEO
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
  2. Parallel computing methodologies
    1. Parallel programming languages
2. Software and its engineering
  1. Software creation and management
    1. Search-based software engineering

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cover image ACM Transactions on Software Engineering and Methodology

ACM Transactions on Software Engineering and Methodology Volume 30, Issue 2

Continuous Special Section: AI and SE

April 2021

463 pages

ISSN:1049-331X

EISSN:1557-7392

DOI:10.1145/3446657

Editor:
Mauro Pezzè
Università della Svizzera italiana and Università di Milano-Bicocca, Switzerland

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Publication History

Published: 03 January 2021

Accepted: 01 September 2020

Revised: 01 September 2020

Received: 01 January 2020

Published in TOSEM Volume 30, Issue 2

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https://dl.acm.org/doi/10.1145/3639478.3643067
Wang YCui WWang GWang JGong D(2024)Improving Test Data Generation for MPI Program Path Coverage With FERPSO-IMPR and Surrogate-Assisted ModelsIEEE Transactions on Software Engineering10.1109/TSE.2024.335497150:3(495-511)Online publication date: Mar-2024
https://doi.org/10.1109/TSE.2024.3354971
Dang XWang JGong DYao XWei CXu B(2024)Test data generation for covering mutation-based path using MGA for MPI programJournal of Systems and Software10.1016/j.jss.2024.111962210:COnline publication date: 1-Apr-2024
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Dang XGong DYao XTian TLiu H(2022)Enhancement of Mutation Testing via Fuzzy Clustering and Multi-Population Genetic AlgorithmIEEE Transactions on Software Engineering10.1109/TSE.2021.305298748:6(2141-2156)Online publication date: 1-Jun-2022
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