Machine learning based prediction of complex bugs in source code
Joint Authors
Uqayli, Ishrat Un Nisa
Ahsan, Sayyid Nadim
Source
The International Arab Journal of Information Technology
Issue
Vol. 17, Issue 1 (31 Jan. 2020), pp.26-37, 12 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2020-01-31
Country of Publication
Jordan
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
During software development and maintenance phases, the fixing of severe bugs are mostly very challenging and needs more efforts to fix them on a priority basis.
Several research works have been performed using software metrics and predict fault-prone software module.
In this paper, we propose an approach to categorize different types of bugs according to their severity and priority basis and then use them to label software metrics’ data.
Finally, we used labeled data to train the supervised machine learning models for the prediction of fault prone software modules.
Moreover, to build an effective prediction model, we used genetic algorithm to search those sets of metrics which are highly correlated with severe bugs.
American Psychological Association (APA)
Uqayli, Ishrat Un Nisa& Ahsan, Sayyid Nadim. 2020. Machine learning based prediction of complex bugs in source code. The International Arab Journal of Information Technology،Vol. 17, no. 1, pp.26-37.
https://search.emarefa.net/detail/BIM-955147
Modern Language Association (MLA)
Uqayli, Ishrat Un Nisa& Ahsan, Sayyid Nadim. Machine learning based prediction of complex bugs in source code. The International Arab Journal of Information Technology Vol. 17, no. 1 (Jan. 2020), pp.26-37.
https://search.emarefa.net/detail/BIM-955147
American Medical Association (AMA)
Uqayli, Ishrat Un Nisa& Ahsan, Sayyid Nadim. Machine learning based prediction of complex bugs in source code. The International Arab Journal of Information Technology. 2020. Vol. 17, no. 1, pp.26-37.
https://search.emarefa.net/detail/BIM-955147
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 35-37
Record ID
BIM-955147