Predicting the severity of a reported bug

By Ahmed Lamkanfi, Serge Demeyer, Emanuel Giger, Bart Goethals. IEEE Working Conference on Mining Software Repositories, 2010

361 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/0ce44dab56974c898a6b26fe0ee89f3bcde184d4

How researchers see this paper

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

In Information Systems

1. Mika V. Mäntylä
2. Marco Zanoni
3. Francesca Arcelli Fontana
4. Alessandro Marino

Citations in the corpus

• Comparing and experimenting machine learning techniques for code smell detection (2015, background): “Several authors have recently worked on the prediction of defect severity by means of machine learning techniques (Menzies and Marcus 2008) (Lamkanfi and Demeyer 2010) (Tian et al.”
• A Systematic Literature Review on Fault Prediction Performance in Software Engineering (2012, neutral): “[24], Singh et al.”
• RCLinker: Automated Linking of Issue Reports and Commits Leveraging Rich Contextual Information (2015, neutral): “Menzies and Marcus, Lamkafi et al. , and Tian et al. made use of various classification algorithms to predict the values of the severity fields of bug reports [42], [31], [32], [57].”