Correlation-based modified long short-term memory network approach for software defect prediction

SK Pemmada, HS Behera, J Nayak, B Naik - Evolving Systems, 2022 - Springer
Developing software applications has become more perplexing nowadays due to the huge
usage of software applications. Under such circumstances, developing software without …

A new approach to software defect prediction based on convolutional neural network and bidirectional long short-term memory

K Nehéz, NAA Khleel - Production Systems and Information …, 2022 - ojs.uni-miskolc.hu
Software defect prediction (SDP) plays an important role in improving software quality and
reliability while reducing software maintenance cost. The problem in the field of SDP is how …

Software defect prediction using a bidirectional LSTM network combined with oversampling techniques

NAA Khleel, K Nehéz - Cluster Computing, 2023 - Springer
Software defects are a critical issue in software development that can lead to system failures
and cause significant financial losses. Predicting software defects is a vital aspect of …

[PDF][PDF] Software defect prediction using deep learning

M Nevendra, P Singh - Acta Polytechnica Hungarica, 2021 - researchgate.net
An increasing number of defects in software, damages the quality and reliability of that
software. The detection of defective instances is becoming increasingly important, and …

An improved CNN-based architecture for within-project software defect prediction

R Malohtra, HS Yadav - … Computing and Signal Processing: Proceedings of …, 2021 - Springer
To improve the software quality, the software is generally tested to find out any bugs or a
simple reliability test. A reliable software defect checking mechanism is a leading research …

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 …

[HTML][HTML] Ensemble learning based software defect prediction

X Dong, Y Liang, S Miyamoto, S Yamaguchi - Journal of Engineering …, 2023 - Elsevier
Currently, the cost to detect and solve software defects is a heavy burden on software
projects. So, it is significant to predict software defects at the earlier stages of the software …

A framework for software defect prediction using optimal hyper-parameters of deep neural network

R Kumar, A Chaturvedi - International Conference on Neural Information …, 2022 - Springer
Software defect prediction (SDP) models are widely used to identify the defect-prone
modules in the software system. SDP model can help to reduce the testing cost, resource …

Software defect prediction: an ensemble learning approach

Z Yang, C Jin, Y Zhang, J Wang… - Journal of Physics …, 2022 - iopscience.iop.org
Software defect prediction plays an increasingly critical role in emerging software systems.
However, existing software defect prediction approaches typically suffer from low accuracy …

A novel convolutional neural network model to predict software defects

K Rajnish, V Bhattacharjee… - … and Methods of Machine …, 2022 - Wiley Online Library
Machine learning (ML) is becoming increasingly important as a research tool due to its
various frameworks and learning approaches. With the ever‐increasing scale of software …