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 …
LDFR: Learning deep feature representation for software defect prediction
Abstract Software Defect Prediction (SDP) aims to detect defective modules to enable the
reasonable allocation of testing resources, which is an economically critical activity in …
reasonable allocation of testing resources, which is an economically critical activity in …
Generative adversarial networks-based imbalance learning in software aging-related bug prediction
SS Chouhan, SS Rathore - IEEE Transactions on Reliability, 2021 - ieeexplore.ieee.org
Software aging refers to a problem of performance decay in the software systems, which are
running for a long period. The primary cause of this phenomenon is the accumulation of run …
running for a long period. The primary cause of this phenomenon is the accumulation of run …
SMOTEFRIS-INFFC: Handling the challenge of borderline and noisy examples in imbalanced learning for software defect prediction
Abstract The object of Software Defect Prediction (SDP) is to identify modules that are prone
to defect. This is achieved by training prediction models with datasets obtained by mining …
to defect. This is achieved by training prediction models with datasets obtained by mining …
An ensemble learning approach for software defect prediction in developing quality software product
Abstract Software Defect Prediction (SDP) is a major research field in the software
development life cycle. The accurate SDP would assist software developers and engineers …
development life cycle. The accurate SDP would assist software developers and engineers …
Asymmetric learning based on kernel partial least squares for software defect prediction
Asymmetric Learning Based on Kernel Partial Least Squares for Software Defect Prediction
Page 1 2006 IEICE TRANS. INF. & SYST., VOL.E95–D, NO.7 JULY 2012 LETTER Asymmetric …
Page 1 2006 IEICE TRANS. INF. & SYST., VOL.E95–D, NO.7 JULY 2012 LETTER Asymmetric …
Ensemble machine learning paradigms in software defect prediction
Predicting faults in software aims to detect defects before the testing phase, allowing for
better resource allocation and high-quality software development, which is a requisite for …
better resource allocation and high-quality software development, which is a requisite for …
The impact of software fault prediction in real-world application: an automated approach for software engineering
Software fault prediction and proneness has long been considered as a critical issue for the
tech industry and software professionals. In the traditional techniques, it requires previous …
tech industry and software professionals. In the traditional techniques, it requires previous …
Studying the effectiveness of deep active learning in software defect prediction
F Feyzi, A Daneshdoost - International Journal of Computers and …, 2023 - Taylor & Francis
Accurate prediction of defective software modules is of great importance for prioritizing
quality assurance efforts, reasonably allocating testing resources, reducing costs and …
quality assurance efforts, reasonably allocating testing resources, reducing costs and …
Class imbalance in software fault prediction data set
Classification has been the prominent technique in machine learning domain, due to its
ability of forecasting and predicts capabilities it is widely used in various domains such as …
ability of forecasting and predicts capabilities it is widely used in various domains such as …