A systematic review of machine learning techniques for software fault prediction

By Ruchika Malhotra. Applied Soft Computing, 2015

608 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/00595e7fc0efcfa6f932956f22392a5933a9e4a9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Yang Xiang
2. Jun Zhang
3. Qing-Long Han
4. Sheng Wen
5. Taghi M. Khoshgoftaar
6. Guanjun Lin
7. Hamid Reza Shahriari
8. Seyed Mohammad Ghaffarian

In Information Systems

1. Sheng Wen
2. Qing-Long Han
3. Taghi M. Khoshgoftaar
4. Guanjun Lin
5. Jun Zhang
6. Yang Xiang
7. Hamid Reza Shahriari
8. Seyed Mohammad Ghaffarian

In Software

1. Yang Xiang
2. Jun Zhang
3. Sheng Wen
4. Qing-Long Han
5. Guanjun Lin
6. Hamid Reza Shahriari
7. Seyed Mohammad Ghaffarian

Citations in the corpus

• A survey on addressing high-class imbalance in big data (2018, background): “Feature Selection Methods, while largely used only (without consideration to class imbalance) to improve classification performance [19, 20], may also help select the most influential features (or attributes) that can yield unique knowledge for inter-class discrimination.”
• Software Vulnerability Detection Using Deep Neural Networks: A Survey (2020, methodology): “[64] R.”
• Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques (2017, background): “Extensive research is performed and published on the subject of fault prediction models in the domain of software engineering that are reviewed in survey articles (Catal and Diri 2009; Malhotra 2015).”