Optimization of Genetic Algorithm Performance Using Naïve Bayes for Basis Path Generation
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Optimization of Genetic Algorithm Performance Using Naïve Bayes for Basis Path Generation

Achmad Arwan, Denny Sagita Rusdianto


Basis path testing is a method used to identify code defects. The determination of independent paths on basis path testing can be generated by using Genetic Algorithm. However, this method has a weakness. In example, the number of iterations can affect the emersion of basis path. When the iteration is low, it results in the incomplete path occurences.  Conversely, if iteration is plentiful resulting to path occurences, after a certain iteration, unfortunately, the result does not change. This study aims to perform the optimization of Genetic Algorithm performance for independent path determination by determining how many iteration levels match the characteristics of the code. The characteristics of the code used include Node, Edge, VG, NBD, and LOC. Moreover, Naïve Bayes is a method used to predict the exact number of iterations based on 17 selected code data into training data, and 16 data into test data. The result of system accuracy test is able to predict the exact iteration of 93.75% from 16 test data. Time-test results show that the new system was able to complete an independent search path being faster 15% than the old system.


Basis Path Testing, Genetic Algorithm Genetik, Naive Bayes

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[1] I. Sommerville, "Software Engineering," 9th ed. Pearson, 2011.

[2] V. Elodie, "White Box Coverage and Control Flow Graphs," Pp. 1–33, 2011.

[3] A. Bertolino, R. Mirandola, and E. Peciola, "A Case Study in Branch Testing Automation, Journal of Systems and Software," Vol. 38, No. 1, Pp. 47–59, 1997.

[4] F. Zapata, A. Akundi, R. Pineda, and E. Smith, "Basis Path Analysis for Testing Complex System of Systems, Procedia Computer Science," Vol. 20, Pp. 256–261, 2013.

[5] A. Ghiduk, M. R. Girgis, and A. S. Ghiduk, "Automatic Generation of Data Flow Test Paths Using a Genetic Algorithm," February, 2014.

[6] W. Xibo and S. Na, "Automatic Test Data Generation for Path Testing Using Genetic Algorithms,” 2011.

[7] I. Rash, “An Empirical Study of the Naive {Bayes} Classifier,” January 2001, 2001.

[8] S. Herbold, J. Grabowski, and S. Waack, “Calculation and Optimization of Thresholds for Sets of Software Metrics,” Empirical Software Engineering, Vol. 16, No. 6, Pp. 812–841, 2011.

[9] T. Ostrand, “White-Box Testing,” Encyclopedia of Software Engineering, 2002.

[10] D. Kafura and G. R. Reddy, “The Use of Software Complexity Metrics in Software Maintenance,” IEEE Transactions on Software Engineering, Vol. SE-13, No. 3, Pp. 335–343, 1987.

[11] A. Arwan, M. Sidiq, B. Priyambadha, H. Kristianto, and R. Sarno, “Ontology and Semantic Matching for Diabetic Food Recommendations,” Proceedings - 2013 International Conference on Information Technology and Electrical Engineering: "Intelligent and Green Technologies for Sustainable Development", ICITEE 2013, Pp. 170–175, October, 2013.

[12] R. Pawlak et al., “Spoon: A Library for Implementing Analyses and Transformations of Java Source Code,” 2015.

[13] E. Frank, M. A. Hall, and I. H. Witten, “The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques,” Morgan Kaufmann, Fourth Ed., 2016.


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