Transfer defect learning

By Jaechang Nam, Sinno Jialin Pan, Sunghun Kim. International Conference on Software Engineering, 2013

541 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/09aa40d09924cc9d23830cbb4c5a1d78cfa1d70c

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. Jaechang Nam
2. David Lo
3. Wei Fu
4. Lin Tan
5. Xin Xia
6. Sunghun Kim
7. Tim Menzies
8. Michael R. Lyu

In Software

1. Jaechang Nam
2. David Lo
3. Wei Fu
4. Lin Tan
5. Sunghun Kim
6. Tim Menzies
7. Xin Xia
8. Yang Xiang

Citations in the corpus

• Deep Learning for Just-in-Time Defect Prediction (2015, methodology): “We use cost effectiveness and F1-score which are also used by past software engineering studies to evaluate the effectiveness of various prediction techniques [7], [12], [16], [33], [34], [17], [14], [13], [35].”
• Software Vulnerability Detection Using Deep Neural Networks: A Survey (2020, methodology): “For the cross-project scenario, the authors chose the TCA + [71] as the baseline system.”
• Heterogeneous Defect Prediction (2015, methodology): “For the machine learning algorithm, we use seven widely used classifiers such as Simple logistic, Logistic regression, Random Forest, Bayesian Network, Support vector machine, J48 decision tree, and Logistic model tree [1], [7], [8], [77], [77], [78], [79].”
• An empirical study on software defect prediction with a simplified metric set (2014, methodology): “In addition, there are many other commonly-used methods in machine learning literature, such as min-max and z-score [47].”
• Software Defect Prediction via Convolutional Neural Network (2017, background): “[7], who adopted a state-of-the-art transfer learning technique called Transfer Component Analysis (TCA).”
• Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques (2017, background): “of machine-learning research as “inductive transfer” (or “transfer learning”) techniques (Pan and Yang 2010), which their use has been investigated in software defect prediction studies (Ma et al. 2012; Nam et al. 2013).”