Software Defect Prediction Using Artificial Neural Networks: A Systematic Literature Review
Elmitwally, Nouh and Khan, Muhammad Adnan and Abbas, Sagheer and Aftab, Shabib and Ahmad, Munir and Fayaz, Muhammad and Khan, Faheem (2022) Software Defect Prediction Using Artificial Neural Networks: A Systematic Literature Review. Scientific Programming, 2022. ISSN 1058-9244
|
Text
Software Defect Prediction Using Artificial Neural Networks A Systematic Literature Review.pdf - Published Version Available under License Creative Commons Attribution. Download (554kB) |
Abstract
The demand for automated online software systems is increasing day by day, which triggered the need for high-quality and maintainable softwares at lower cost. Software defect prediction is one of the crucial tasks of the quality assurance process which improves the quality at lower cost by reducing the overall testing and maintenance efforts. Early detection of defects in the software development life cycle (SDLC) leads to the early corrections and ultimately timely delivery of maintainable software, which satisfies the customer and makes him confident towards the development team. In the last decade, many machine learning-based approaches for software defect prediction have been proposed to achieve the higher accuracy. Artificial Neural Network (ANN) is considered as one of the widely used machine learning techniques, which is included in most of the proposed defect prediction frameworks and models. This research provides a critical analysis of the latest literature, published from year 2015 to 2018 on the use of Artificial Neural Networks for software defect prediction. In this study, a systematic research process is followed to extract the literature from three widely used digital libraries including IEEE, Elsevier, and Springer, and then after following a thorough process, 8 most relevant research publications are selected for critical review. This study will serve the researchers by exploring the current trends in software defect prediction with the focus on ANNs and will also provide a baseline for future innovations, comparisons, and reviews.
| Item Type: | Article | ||||||
|---|---|---|---|---|---|---|---|
| Identification Number: | https://doi.org/10.1155/2022/2117339 | ||||||
| Dates: |
|
||||||
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence |
||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology | ||||||
| Depositing User: | Nouh Elmitwally | ||||||
| Date Deposited: | 31 Aug 2022 14:36 | ||||||
| Last Modified: | 31 Aug 2022 14:36 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/13505 |
Actions (login required)
 |
View Item |
Tools
Tools