This study explores the application of deep learning technologies in software development
processes, particularly in automating code reviews, error prediction, and test generation to …

In software development, the main problem is recognizing the security-oriented issues within
the reported bugs due to their unacceptable failure rate to provide satisfactory reliability on …

As software systems undergo escalating complexity, the identification of bugs and defects
becomes pivotal for ensuring seamless user experiences and averting potentially costly post …

The identification of anomalies in a data stream is a difficulty for decision-making in real
time. A memory-constrained online detection system that is able to quickly detect the …

Software defect prediction is one of the essential sets of operation towards mitigating issues
of risk management in software development known to contribute towards enhancing the …

In this work, we investigate a unique effective framework for projected design inspections in
industrial manufacturing combining machine learning methodologies and edges cloud …

Today, there is a contradiction between the rapid increase in the complexity and size of
modern software while increasing responsibility for the performance of their functions, the …

Software quality assurance is a set of activities that ensures the quality of the software from
different aspects. This process continues in parallel with the development of the software to …

Federated learning is a paradigm for distributed machine learning in which a central server
interacts with a large number of remote devices to create the optimal global model. System …

With the rapid advancement of science and technology, the number of edge devices with
computing and storage capabilities, as well as the generated data traffic, continues to …