In this research, authors give a literature analysis of the methods used to detect and
anticipate security risks in software testing by using a number of deep learning models. The …

The exponential growth in data over the past decade has impacted the legal industry; both
requiring automated solutions for the cost effective and efficient management of the volume …

Many organizations seek to ensure that machine learning (ML) and artificial intelligence (AI)
systems work as intended in production but currently do not have a cohesive methodology in …

Losing Confidence in Quality: Unspoken Evolution of Computer Vision Services Page 1 Losing
Confidence in Quality: Unspoken Evolution of Computer Vision Services Alex Cummaudo∗ …

Software Testing is a well-established area in software engineering, encompassing various
techniques and methodologies to ensure the quality of software systems. However, with the …

Context: Deep learning has achieved remarkable progress in various domains. However,
like traditional software systems, deep learning systems contain bugs, which can have …

Deep Learning applications are becoming increasingly popular worldwide. Developers of
deep learning systems like in every other context of software development strive to write …

Abstract Machine learning is nowadays a standard technique for data analysis within
software applications. Software engineers need quality assurance techniques that are …

Deep Learning (DL) has revolutionized the capabilities of vision-based systems (VBS) in
critical applications such as autonomous driving, robotic surgery, critical infrastructure …

In the last few years, Internet of Things (IoT) systems have drastically increased their
relevance in many fundamental sectors. For this reason, assuring their quality is of …