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Efficacy of Inheritance Aspect in Software Fault Prediction—A Survey Paper

Authors :
Syed Rashid Aziz
Tamim Ahmed Khan
Aamer Nadeem
Source :
IEEE Access, Vol 8, Pp 170548-170567 (2020)
Publication Year :
2020
Publisher :
IEEE, 2020.

Abstract

Software fault prediction (SFP) is a research area that helps development and testing process deliver software of good quality. Software metrics are of various types and are used in SFP for measurements. Inheritance is a prominent feature, which measures the depth, breadth, and complexity of object-oriented software. A few studies exclusively addressed the efficacy of inheritance in SFP. This provokes the need to identify the potential ingredients associated with inheritance, which can be helpful in SFP. In this paper, our aim is to collecting, organizing, categorizing, and investigating published fault prediction studies. Findings include identification of 54 inheritance metrics, 78 public datasets with various combinations of 10 inheritance metrics, 60% use of method level & use of private datasets, an increased number of studies using machine learning approaches. This study will facilitate scholars to studying previous literature on software fault prediction having software metrics, with their methods, public data sets, performance evaluation of machine learning algorithms, and findings of experimental results in a comfortable, and efficient way, emphasizing the inherited aspect specifically.

Details

Language :
English
ISSN :
21693536
Volume :
8
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.6237b8dcb6114805ac7e1cf2f9d8c0d7
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2020.3022087