Name
Title | ||
|---|---|---|
Iterated feature selection algorithms with layered recurrent neural network for software fault prediction. |
Abstract | ||
|---|---|---|
•Fault prediction improves the effectiveness of software quality assurance activities.•This paper focuses on building an effective fault prediction classifier.•Fault prediction model using Iterated feature selection algorithms with L-RNN.•We perform experiments on 19 open source projects.•Fault prediction model is best suitable for projects with faulty classes less than the threshold value. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.eswa.2018.12.033 | Expert Systems with Applications |
Keywords | Field | DocType |
Software fault prediction,Feature selection,Layered recurrent neural network | Ant colony optimization algorithms,Data mining,Decision tree,Feature selection,Naive Bayes classifier,Computer science,Algorithm,Recurrent neural network,Software,Component-based software engineering,Artificial neural network | Journal |
Volume | ISSN | Citations |
122 | 0957-4174 | 5 |
PageRank | References | Authors |
0.38 | 40 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hamza Turabieh | 1 | 136 | 11.41 |
| Majdi Mafarja | 2 | 574 | 20.00 |
| Xiaodong Li | 3 | 428 | 40.14 |