A Survey of Genetic Feature Selection for Software Defect Prediction

R. Reena*, R. Thirumalaiselvi**
* Research Scholar, Department of Computer Science, Government Arts College, Nandanam, Chennai, India.
** Assistant Professor, Department of Computer Science, Government Arts College, Nandanam, Chennai, India.
Periodicity:January - March'2016
DOI : https://doi.org/10.26634/jse.10.3.4901

Abstract

Software defect prediction is an important research topic in the software engineering field, especially to solve the inefficiency and ineffectiveness of the existing industrial approach of software testing and reviews. The software defect prediction performance decreases significantly because the data set contains noisy attributes and class imbalance. Feature selection is generally used in machine learning when the learning task involves high-dimensional and noisy attribute datasets. In this survey, a Genetic Algorithm and a bagging technique is a research topic for Software Defect Prediction. The survey of publications on this topic leads to the conclusion that the field of genetic algorithms applications is growing fast. The authors overall aim is to provide an efficient feature selection for further development of the research.

Keywords

Software Defect Prediction, Genetic Algorithm, Feature Selection, Bagging Technique.

How to Cite this Article?

Reena, R., and Selvi, R. T. (2016). A Survey of Genetic Feature Selection for Software Defect Prediction. i-manager’s Journal on Software Engineering, 10(3), 20-26. https://doi.org/10.26634/jse.10.3.4901

References

[1]. Mitchell Melanie, (1999). Genetic Algorithm Introduction. A Bradford Book, The MIT Press Cambridge, Massachusetts, London, England Fifth Printing.
[2]. Chayanika Sharma and Sangeeta Sabharwal, (2013). "A Survey on Software Testing Techniques using Genetic Algorithm". IJCSI International Journal of Computer Science Issues, Vol.10(1), No.1.
[3]. Nikita Kravtsov and Maxim Buzdalov, (2014). "Worst- Case Execution Time Test Generation using Genetic Algorithms with Automated Construction and Online th Selection of Objectives". 20 International Conference on Soft Computing MENDEL 2014, Brno, Czech Republic, June 25 -27.
[4]. AditiPuri and Harshpreet Singh, (2014). "Genetic Algorithm Based Approach for Finding Faulty Modules in Open Source Software Systems". International Journal of Computer Science & Engineering Survey (IJCSES), Vol.5, No.3.
[5]. K. Devika Rani Dhivya and C. Sunitha, (2014). "A Review on Optimization Methodologies Used for Randomized Unit Testing". International Journal of Advanced Research in Computer Science and Software Engineering, Vol.4, No.6, pp.748-752.
[6]. Isatou Hydara, Abu Bakar Md Sultan and Hazura Zulzalil, (2014). "An Approach for Cross-Site Scripting Detection and Removal Based on Genetic Algorithms". The Ninth International Conference on Software Engineering Advances ICSEA.
[7]. PoonamSaini and Sanjay Tyagi, (2014). "Test Data Generation for Basis Path Testing using Genetic Algorithm and Clonal Selection Algorithm". International Journal of Science and Research (IJSR), Vol.3, No.6.
[8]. Kriti Singh and ParamjeetKaur, (2014). "Efficient Test Cases of Regression Testing using Genetic Algorithm". International Journal of Advanced Research in Computer and Communication Engineering, Vol.3, No.7.
[9]. A.M. Sherry and Manish Saraswat, (2014). "Test Suites Prioritization for Regression Testing using Genetic Algorithm". IJETCAS, pp.14-150.
[10]. J. Srividhya and K. Alagarsamy, (2014). "Modified Genetic Approach for Regression Testing Cost Reduction". International Journal of Infinite Innovations in Engineering and Technology, Vol.1, No.1.
[11]. Ravneet Kaur, (2014). "Multi-Objective Genetic Algorithm For Regression Testing Reduction". IJRET: International Journal of Research in Engineering and Technology, Vol.3, No.1.
[12]. Kirandeep Kaur and Vinay Chopra, (2014). "Review of Automatic Test Case Generation from UML Diagram using Evolutionary Algorithm". International Journal of Inventive Engineering and Sciences (IJIES), ISSN: 2319–9598, Vol.2, No.11.
[13]. E. Osaba And R. Carballedo, (2014). "On the influence of using initialization functions on genetic algorithms solving combinatorial optimization problems: a first study on the TSP". IEEE Conference on Evolving and Adaptive Intelligent Systems.
[14]. Chayanika Sharma and Sangeeta Sabharwal, (2013). "A Survey on Software Testing Techniques using Genetic Algorithm". IJCSI International Journal of Computer Science, Vol.10, No.1.
[15]. Josh Kounitz, “Understanding Software Test Cases”.
[16]. Rijwan Khan and Mohd Amjad, (2014). "Automated Test Case Generation using Nature Inspired Meta Heuristics- Genetic Algorithm: A Review Paper". International Journal of Application or Innovation in Engineering & Management (IJAIEM), Vol.3, No.11.
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