A survey of transfer learning
K Weiss, TM Khoshgoftaar, DD Wang - Journal of Big data, 2016 - Springer
Abstract Machine learning and data mining techniques have been used in numerous real-
world applications. An assumption of traditional machine learning methodologies is the …
world applications. An assumption of traditional machine learning methodologies is the …
Software fault prediction using data mining, machine learning and deep learning techniques: A systematic literature review
Software fault/defect prediction assists software developers to identify faulty constructs, such
as modules or classes, early in the software development life cycle. There are data mining …
as modules or classes, early in the software development life cycle. There are data mining …
Review of classification methods on unbalanced data sets
L Wang, M Han, X Li, N Zhang, H Cheng - Ieee Access, 2021 - ieeexplore.ieee.org
This paper studies the classification of unbalanced data sets. First, this kind of data sets is
briefly introduced, and then the classification methods of unbalanced data sets are analyzed …
briefly introduced, and then the classification methods of unbalanced data sets are analyzed …
Deepfl: Integrating multiple fault diagnosis dimensions for deep fault localization
Learning-based fault localization has been intensively studied recently. Prior studies have
shown that traditional Learning-to-Rank techniques can help precisely diagnose fault …
shown that traditional Learning-to-Rank techniques can help precisely diagnose fault …
Heterogeneous defect prediction
Software defect prediction is one of the most active research areas in software engineering.
We can build a prediction model with defect data collected from a software project and …
We can build a prediction model with defect data collected from a software project and …
A survey on heterogeneous transfer learning
O Day, TM Khoshgoftaar - Journal of Big Data, 2017 - Springer
Transfer learning has been demonstrated to be effective for many real-world applications as
it exploits knowledge present in labeled training data from a source domain to enhance a …
it exploits knowledge present in labeled training data from a source domain to enhance a …
Progress on approaches to software defect prediction
Software defect prediction is one of the most popular research topics in software
engineering. It aims to predict defect‐prone software modules before defects are discovered …
engineering. It aims to predict defect‐prone software modules before defects are discovered …
Software defect prediction based on kernel PCA and weighted extreme learning machine
Context Software defect prediction strives to detect defect-prone software modules by mining
the historical data. Effective prediction enables reasonable testing resource allocation …
the historical data. Effective prediction enables reasonable testing resource allocation …
Tuning for software analytics: Is it really necessary?
Context: Data miners have been widely used in software engineering to, say, generate
defect predictors from static code measures. Such static code defect predictors perform well …
defect predictors from static code measures. Such static code defect predictors perform well …
A study on software fault prediction techniques
SS Rathore, S Kumar - Artificial Intelligence Review, 2019 - Springer
Software fault prediction aims to identify fault-prone software modules by using some
underlying properties of the software project before the actual testing process begins. It …
underlying properties of the software project before the actual testing process begins. It …