Within the vast expanse of computerized language processing, a revolutionary entity known
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …

Within the vast expanse of computerized language processing, a revolutionary entity known
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …

Context: Automated software defect prediction (SDP) methods are increasingly applied,
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …

Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone
areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a …

Software defects not only reduce operational reliability but also significantly increase overall
maintenance costs. Consequently, it is necessary to predict software defects at an early …

Software fault prediction (SFP) aims to improve software quality with a possible minimum
cost and time. Various machine learning models have been proposed in the past for …

Machine learning (ML) techniques increase the effectiveness of software engineering (SE)
lifecycle activities. We systematically collected, quality-assessed, summarized, and …

Deep neural networks (DNNs) are widely used in various application domains such as
image processing, speech recognition, and natural language processing. However, testing …

The integration of renewable energy sources into power grids is a critical challenge for
developing economies, where infrastructure limitations, unpredictable energy demand, and …

Context: Machine learning software defect prediction is a promising field of software
engineering, attracting a great deal of attention from the research community; however, its …