Author: Jacinth Paul

Deep learning neural networks (DLNN) are a subset of machine learning techniques that model high-level abstractions in data through multiple layers of interconnected nodes. These networks can automatically learn representations from raw data, enabling them to perform tasks such as image and speech recognition, natural language processing, and game playing. This section provides an overview of DLNN, discusses its role in automated test case generation and optimization, and highlights successful applications and future trends in this area.

Structure of Deep Learning Neural Networks

DLNN consists of interconnected layers of artificial neurons, also known as nodes. These layers can be grouped into three categories:

The rapid growth of technology has led to an increased demand for efficient and effective software testing methods. One of the most promising advancements in this field is the integration of Natural Language Processing (NLP) techniques. NLP, a subset of artificial intelligence (AI), is focused on the interaction between computers and humans through natural language. In the context of software testing, NLP offers the potential to automate test case creation and documentation, ultimately reducing the time, effort, and costs associated with manual testing processes.

This article explores the benefits and challenges of using NLP in software testing, focusing on automating test case creation and documentation. We will discuss the key NLP techniques used in this area, real-world applications, and the future of NLP in software testing.