Data Quality: Some Comments on the NASA Software Defect Datasets
By Martin J. Shepperd, Qinbao Song, Zhongbin Sun, C. Mair. IEEE Transactions on Software Engineering, 2013
538 citations recorded by the source corpus.
Subfields: Information Systems, Software
View Paper: https://www.semanticscholar.org/paper/2ad27a03e7c120c3d1fe31368a7a897960afd8cb
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
- Neel Sundaresan
- Alexey Svyatkovskiy
- Shao Kun Deng
- Baljinder Ghotra
In Software
- Chakkrit Kla Tantithamthavorn
- Ken-ichi Matsumoto
- Ahmed E. Hassan
- Shane McIntosh
- Neel Sundaresan
- Alexey Svyatkovskiy
- Shao Kun Deng
- Baljinder Ghotra
Citations in the corpus
- The Impact of Automated Parameter Optimization on Defect Prediction Models (2018, methodology): “[123], 5 datasets as provided by Kim et al.”
- Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models (2015, methodology): “[57] applied to the noisy NASA corpus are presented in Table VI.”
- METHODS2TEST: A dataset of focal methods mapped to test cases (2022, methodology): “Examples of such datasets include Defects4J [14], Bugs2Fix [27], and CodRep [5] for automated bug-fixing, NASA Metrics Data Program (MDP) [23], PROMISE [2], and the unified dataset [9] for defect prediction, Landfill [19] for code smell detection, and many others.”
- The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, methodology): “[27], 5 datasets are provided by Kim et al.”
- Researcher Bias: The Use of Machine Learning in Software Defect Prediction (2014, background): “These were added since we are able to obtain additional information concerning the NASA data sets from other sources and although concerns have been raised about data quality this is in part due to the fact that they have been more thoroughly scrutinised than other data sets [12], [13].”
- An Empirical Comparison of Model Validation Techniques for Defect Prediction Models (2017, background): “The conclusions that are derived from defect prediction models may not be sound if the estimated performance is unrealistic or unstable.”