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Software Defect Prediction Using Genetic Programming and Neural Networks

Software Defect Prediction Using Genetic Programming and Neural Networks

Mohammed Akour, Wasen Yahya Melhem
Copyright: © 2017 |Volume: 8 |Issue: 4 |Pages: 20
ISSN: 1942-3926|EISSN: 1942-3934|EISBN13: 9781522512707|DOI: 10.4018/IJOSSP.2017100102
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MLA

Akour, Mohammed, and Wasen Yahya Melhem. "Software Defect Prediction Using Genetic Programming and Neural Networks." IJOSSP vol.8, no.4 2017: pp.32-51. http://doi.org/10.4018/IJOSSP.2017100102

APA

Akour, M. & Melhem, W. Y. (2017). Software Defect Prediction Using Genetic Programming and Neural Networks. International Journal of Open Source Software and Processes (IJOSSP), 8(4), 32-51. http://doi.org/10.4018/IJOSSP.2017100102

Chicago

Akour, Mohammed, and Wasen Yahya Melhem. "Software Defect Prediction Using Genetic Programming and Neural Networks," International Journal of Open Source Software and Processes (IJOSSP) 8, no.4: 32-51. http://doi.org/10.4018/IJOSSP.2017100102

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Abstract

This article describes how classification methods on software defect prediction is widely researched due to the need to increase the software quality and decrease testing efforts. However, findings of past researches done on this issue has not shown any classifier which proves to be superior to the other. Additionally, there is a lack of research that studies the effects and accuracy of genetic programming on software defect prediction. To find solutions for this problem, a comparative software defect prediction experiment between genetic programming and neural networks are performed on four datasets from the NASA Metrics Data repository. Generally, an interesting degree of accuracy is detected, which shows how the metric-based classification is useful. Nevertheless, this article specifies that the application and usage of genetic programming is highly recommended due to the detailed analysis it provides, as well as an important feature in this classification method which allows the viewing of each attributes impact in the dataset.

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