Software defect prediction analysis using machine learning techniques
… used for Software bug prediction [11]. The different classifier is applied with Machine Learning
for … Perreault et al. used five distinct types of classifiers to discover software defects in their …
for … Perreault et al. used five distinct types of classifiers to discover software defects in their …
Deep learning for software defect prediction: A survey
S Omri, C Sinz - … IEEE/ACM 42nd international conference on software …, 2020 - dl.acm.org
… Such information is needed for building accurate fault prediction models. In this survey… fault
prediction, also explaining how in recent studies deep learning algorithms for fault prediction …
prediction, also explaining how in recent studies deep learning algorithms for fault prediction …
An empirical study of model-agnostic techniques for defect prediction models
J Jiarpakdee, CK Tantithamthavorn… - … on Software …, 2020 - ieeexplore.ieee.org
… Abstract—Software analytics have empowered software … as to why the machine learning
techniques make that prediction. A … techniques to explain software defect predictions. …
techniques make that prediction. A … techniques to explain software defect predictions. …
Experimental setup for online fault diagnosis of induction machines via promising IoT and machine learning: Towards industry 4.0 empowerment
… based on utilizing machine learning techniques to suppress … In particular, advanced machine
learning techniques are … processed data based on machine learning techniques through a …
learning techniques are … processed data based on machine learning techniques through a …
Hyper-parameter optimization of classifiers, using an artificial immune network and its application to software bug prediction
… when default settings are used for machine learning classifiers. In this paper, software bug
prediction model is proposed which uses machine learning classifiers in conjunction with the …
prediction model is proposed which uses machine learning classifiers in conjunction with the …
Machine learning empowered security management and quality of service provision in SDN-NFV environment
S Shahzadi, F Ahmad, A Basharat… - Computer …, 2020 - researchportal.port.ac.uk
… is also combined the high performance of supervised learning with the low performance
of unsupervised learning. 1.8 Machine Learning Classifiers Machine Learning empowered …
of unsupervised learning. 1.8 Machine Learning Classifiers Machine Learning empowered …
Self learning-empowered thermal error control method of precision machine tools based on digital twin
… growth, the predictive performance of deep learning (DL) … accuracy with self learning-empowered
thermal error control … thermal error is realized with deep learning. The error mechanism …
thermal error control … thermal error is realized with deep learning. The error mechanism …
A systematic literature review on using machine learning algorithms for software requirements identification on stack overflow
… It also empowers software programmers to utilize such platforms for the recognized
underutilized different tasks of software development lifecycle, eg, software requirements …
underutilized different tasks of software development lifecycle, eg, software requirements …
Reliable deep learning and IoT-based monitoring system for secure computer numerical control machines against cyber-attacks with experimental verification
… and deep learning techniques that drive the smart machine … integrating a developed deep
learning method to monitor the … and machine learning: Towards industry 4.0 empowerment. …
learning method to monitor the … and machine learning: Towards industry 4.0 empowerment. …
Prediction of diabetes empowered with fused machine learning
… Currently, machine-learning (ML) algorithms are … machine learning approach for diabetes
prediction. The conceptual framework consists of two types of models: Support Vector Machine …
prediction. The conceptual framework consists of two types of models: Support Vector Machine …