A systematic review on imbalanced data challenges in machine learning: Applications and solutions
In machine learning, the data imbalance imposes challenges to perform data analytics in
almost all areas of real-world research. The raw primary data often suffers from the skewed …
almost all areas of real-world research. The raw primary data often suffers from the skewed …
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 …
[HTML][HTML] On the use of deep learning in software defect prediction
Context: Automated software defect prediction (SDP) methods are increasingly applied,
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
Cross-project defect prediction using a connectivity-based unsupervised classifier
Defect prediction on projects with limited historical data has attracted great interest from both
researchers and practitioners. Cross-project defect prediction has been the main area of …
researchers and practitioners. Cross-project defect prediction has been the main area of …
Deep neural network based hybrid approach for software defect prediction using software metrics
C Manjula, L Florence - Cluster Computing, 2019 - Springer
In the field of early prediction of software defects, various techniques have been developed
such as data mining techniques, machine learning techniques. Still early prediction of …
such as data mining techniques, machine learning techniques. Still early prediction of …
Software defect prediction using stacked denoising autoencoders and two-stage ensemble learning
H Tong, B Liu, S Wang - Information and Software Technology, 2018 - Elsevier
Context Software defect prediction (SDP) plays an important role in allocating testing
resources reasonably, reducing testing costs, and ensuring software quality. However …
resources reasonably, reducing testing costs, and ensuring software quality. However …
An empirical study to investigate oversampling methods for improving software defect prediction using imbalanced data
R Malhotra, S Kamal - Neurocomputing, 2019 - Elsevier
Software defect prediction is important to identify defects in the early phases of software
development life cycle. This early identification and thereby removal of software defects is …
development life cycle. This early identification and thereby removal of software defects is …
BPDET: An effective software bug prediction model using deep representation and ensemble learning techniques
SK Pandey, RB Mishra, AK Tripathi - Expert Systems with Applications, 2020 - Elsevier
In software fault prediction systems, there are many hindrances for detecting faulty modules,
such as missing values or samples, data redundancy, irrelevance features, and correlation …
such as missing values or samples, data redundancy, irrelevance features, and correlation …
Performance analysis of feature selection methods in software defect prediction: a search method approach
Software Defect Prediction (SDP) models are built using software metrics derived from
software systems. The quality of SDP models depends largely on the quality of software …
software systems. The quality of SDP models depends largely on the quality of software …
Software defect prediction analysis using machine learning techniques
There is always a desire for defect-free software in order to maintain software quality for
customer satisfaction and to save testing expenses. As a result, we examined various known …
customer satisfaction and to save testing expenses. As a result, we examined various known …