Transfer learning for cross-company software defect prediction

By Ying Ma, Guangchun Luo, Xue Zeng, Aiguo Chen. Information and Software Technology, 2012

451 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/3d36c67c859c76daccac8e1c3c50130e610bec3d

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Citations in the corpus

A systematic review of machine learning techniques for software fault prediction (2015, background): “Hence, total number of 122 studies [1, 2, 20-139] were identified for further processing and analysis.”
An Empirical Comparison of Model Validation Techniques for Defect Prediction Models (2017, unknown): “Through a case study of 18 systems, we find that single-repetition holdout validation tends to produce estimates with 46-229 percent more bias and 53-863 percent more variance than the top-ranked model validation techniques.”
Software defect prediction using ensemble learning on selected features (2015, background): “With the increasing impact of software applications on day-today businesses and activities, software attribute prediction such as effort estimation [1,2], maintainability [3,4], defect [5,6] and quality [7,8] classification are gaining growing interest from both academic and industry communities.”
Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques (2017, background): “This challenge has been addressed in the field 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).”
Heterogeneous Defect Prediction (2015, methodology): “The TNB builds a prediction model by weighting source instances similar to target instances [47].”
The Impact of Automated Parameter Optimization on Defect Prediction Models (2018, background): “[85] point out that the computational cost depends on to the size of the training dataset.”
How, and why, process metrics are better (2013, background): “Portability (also called “cross-project prediction”) recently attracted quite a bit of attention [14, 22, 24, 25, 28]; but we have not found any that compare the portability of different types of metrics.”
An empirical study on software defect prediction with a simplified metric set (2014, methodology): “Vector Machine (SVM), Logistic Regression, and Random Tree [24, 27, 29], and defect prediction using these typical classiﬁers has achieved many useful conclusions. Currently, some improved classiﬁers [26, 54, 65] and hybrid classiﬁers [30, 31] have also been proposed to eectively improve classiﬁcati”