An Empirical Comparison of Model Validation Techniques for Defect Prediction Models
By Chakkrit Kla Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Ken-ichi Matsumoto. IEEE Transactions on Software Engineering, 2017
512 citations recorded by the source corpus.
Subfields: Information Systems, Software
View Paper: https://www.semanticscholar.org/paper/d0b567b95308c0990b259078a535c981e5ffb8e1
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
- Chakkrit Kla Tantithamthavorn
- Ken-ichi Matsumoto
- Ahmed E. Hassan
- Shane McIntosh
- Hailong Sun
- Xudong Liu
- Xu Wang
- Jian Zhang
In Software
- Chakkrit Kla Tantithamthavorn
- Ken-ichi Matsumoto
- Ahmed E. Hassan
- Shane McIntosh
- Hongyu Zhang
- Hailong Sun
- Marco Tulio de Oliveira Valente
- Xu Wang
Citations in the corpus
- What's in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform (2018, background): “, Scott-Knott ESD [64]) may provide additional insights on the factors that impact the classification of a project in a given growth pattern.”
- A Novel Neural Source Code Representation Based on Abstract Syntax Tree (2019, methodology): “Many software engineering methods, such as source code classification [1], [2], code clone detection [3], [4], [5], [6], defect prediction [7], [8] and code summarization [9], [10] have been proposed to improve software development and maintenance.”
- The Impact of Automated Parameter Optimization on Defect Prediction Models (2018, methodology): “Our recent work shows that defect prediction models that are trained using datasets with low EPV values are especially susceptible to unstable results [141].”