Package Level Test Case Minimization for Bug Prediction using Linear Regression Machine Learning Approach
DOI:
https://doi.org/10.26438/ijcse/v7i6.364370Keywords:
Camel 1.6.1, Test Case Minimization, WEKAAbstract
With the growing complexities in Object Oriented (OO) software, the number of bugs present in the software module is increased. In this paper, a technique has been presented for minimization of test cases for the OO systems. The Camel 1.6.1 open source software was used the evaluation of proposed technique. The mathematical model used in the proposed methodology was generated using the open source software WEKA by selecting effective Object Oriented (OO) metrics. Ineffective and effective Object Oriented metrics were recognized by using the techniques based on feature selection to generate test cases that cover fault proneness classes of the software. The defined methodology used only effective metrics for assigning weights to test paths for minimization. The results show the significant improvements.
References
[1] P. Mandal and A. S. Ami, “Selecting Best Attributes for Software Defect Prediction,” no. December 2015, 2019.
[2] S. Puranik, P. Deshpande, and K. Chandrasekaran, “A Novel Machine Learning Approach for Bug Prediction,” Procedia Computer Science, vol. 93, pp. 924–930, 2016.
[3] S. Prateek, A. Pasala, and L. M. Aracena, “Evaluating Performance of Network Metrics for Bug Prediction in Software,” no. December 2013, 2017.
[4] “Promise repository,” 2014. [Online]. Available: http://openscience.us/repo/defect/ck/. [Accessed: 26-Mar-2018].
[5] A. Singh, R. Bhatia, and A. Singhrova, “Taxonomy of machine learning algorithms in software fault Taxonomy of machine learning algorithms in software fault prediction using object oriented metrics prediction using object oriented metrics,” Procedia Computer Science, vol. 132, pp. 993–1001, 2018.
[6] M. Akour and L. Abuwardih, “Test Case Minimization using Genetic Algorithm : Pilot Study,” 8th International Conference on Computer Science and Information Technology (CSIT), pp. 66–70, 2018.
[7] D. L. A. L. Gupta and K. Saxena, “Software bug prediction using object-oriented metrics,” vol. 42, no. 5, pp. 655–669, 2017.
[8] R. Ferenc, “Empirical Validation of Object-Oriented Metrics on Open Source Software for Fault Prediction,” vol. 31, no. 10, pp. 897–910, 2005.
[9] A. Boucher and M. Badri, "Predicting Fault-Prone Classes in Object-Oriented Software: An Adaptation of an Unsupervised Hybrid SOM Algorithm," IEEE International Conference on Software Quality, Reliability and Security (QRS), pp. 306-317, 2017.
[10] S. K. Mohapatra and S. Prasad, "Minimizing test cases to reduce the cost of regression testing," IEEE international Conference on Computing for Sustainable Global Development (INDIACom), pp. 505-509, 2014.
[11] S. Ali, Y. Li, T. Yue and M. Zhang, "An Empirical Evaluation of Mutation and Crossover Operators for Multi-Objective Uncertainty-Wise Test Minimization," IEEE/ACM 10th International Workshop on Search-Based Software Testing (SBST), Buenos Aires, 2017, pp. 21-27, 2017.
[12] A. S. A. Ansari, K. K. Devadkar and P. Gharpure, "Optimization of test suite-test case in regression test," IEEE International Conference on Computational Intelligence and Computing Research, Enathi, 2013, pp. 1-4, 2013.
[13] O. Banias, “Dynamic programming optimization algorithm applied in test case selection,” International Symposium on Electronics and Telecommunications (ISETC), pp. 1–4, 2018.
[14] K. Choudhary and G. N. Purohit, "A Multi-Objective optimization algorithm for uniformly distributed generation of test cases," IEEE International Conference on Computing for Sustainable Global Development (INDIACom), pp. 455-457, 2014.
[15] R. Khan, M. Amjad and A. K. Srivastava, "Optimization of Automatic Generated Test Cases for Path Testing Using Genetic Algorithm," 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), pp. 32-36, 2016.
[16] S. Sun, X. Hou, C. Gao and L. Sun, "Research on optimization scheme of regression testing," Ninth International Conference on Natural Computation (ICNC), pp. 1628-1632, 2013.
[17] P. A. Vikhar, "Evolutionary algorithms: A critical review and its future prospects," International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), pp. 261-265, 2017.
[18] V. Gupta, N. Ganeshan and Tarun K. Singhal, “Developing Software Bug Prediction Models Using Various Software Metrics as the Bug Indicators,” International Journal of Advanced Computer Science and Applications, Vol. 6, no. 2, 2015.
[19] R. Singh, A. Singhrova, and R. Bhatia, “Optimized Test Case Generation for Object Oriented Systems Using Weka Open Source Software,” International Journal of Open Source Software and Processes, vol. 9, no. 3, pp. 15–35, Jul. 2018.
[20] R. Singh, R. K. Bhatia, and A. Singhrova, “Demand Based Test Case Generation for Object Oriented Systems,” IET Software, 2019.
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