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