Software defect prediction using ensemble learning: A systematic literature review
Recent advances in the domain of software defect prediction (SDP) include the integration of
multiple classification techniques to create an ensemble or hybrid approach. This technique …
multiple classification techniques to create an ensemble or hybrid approach. This technique …
[HTML][HTML] A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning
Class imbalance occurs when the class distribution is not equal. Namely, one class is under-
represented (minority class), and the other class has significantly more samples in the data …
represented (minority class), and the other class has significantly more samples in the data …
Heterogeneous stacked ensemble classifier for software defect prediction
S Goyal - 2020 sixth international conference on parallel …, 2020 - ieeexplore.ieee.org
Software defect prediction (SDP) is vital to enhance the software quality with reduced testing
cost. It stresses to put more testing efforts on those modules which are susceptible to defects …
cost. It stresses to put more testing efforts on those modules which are susceptible to defects …
[HTML][HTML] Intelligent decision forest models for customer churn prediction
Customer churn is a critical issue impacting enterprises and organizations, particularly in the
emerging and highly competitive telecommunications industry. It is important to researchers …
emerging and highly competitive telecommunications industry. It is important to researchers …
[HTML][HTML] Empirical analysis of rank aggregation-based multi-filter feature selection methods in software defect prediction
Selecting the most suitable filter method that will produce a subset of features with the best
performance remains an open problem that is known as filter rank selection problem. A …
performance remains an open problem that is known as filter rank selection problem. A …
[HTML][HTML] A cloud-based software defect prediction system using data and decision-level machine learning fusion
This research contributes an intelligent cloud-based software defect prediction system using
data and decision-level machine learning fusion techniques. The proposed system detects …
data and decision-level machine learning fusion techniques. The proposed system detects …
[HTML][HTML] Empirical analysis of tree-based classification models for customer churn prediction
Customer churn is a vital and reoccurring problem facing most business industries,
particularly the telecommunications industry. Considering the fierce competition among …
particularly the telecommunications industry. Considering the fierce competition among …
[HTML][HTML] An adaptive rank aggregation-based ensemble multi-filter feature selection method in software defect prediction
Feature selection is known to be an applicable solution to address the problem of high
dimensionality in software defect prediction (SDP). However, choosing an appropriate filter …
dimensionality in software defect prediction (SDP). However, choosing an appropriate filter …
[HTML][HTML] Empirical analysis of forest penalizing attribute and its enhanced variations for android malware detection
As a result of the rapid advancement of mobile and internet technology, a plethora of new
mobile security risks has recently emerged. Many techniques have been developed to …
mobile security risks has recently emerged. Many techniques have been developed to …
[HTML][HTML] Enhancing software defect prediction: a framework with improved feature selection and ensemble machine learning
M Ali, T Mazhar, A Al-Rasheed, T Shahzad… - PeerJ Computer …, 2024 - peerj.com
Effective software defect prediction is a crucial aspect of software quality assurance,
enabling the identification of defective modules before the testing phase. This study aims to …
enabling the identification of defective modules before the testing phase. This study aims to …