AI-Driven Software Testing - A Deep Learning Perspective

Authors: Chandra Shekhar Pareek

Country: United States

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Abstract: In the ever-evolving landscape of software development, traditional testing methodologies falter under the complexities of modern systems such as microservices and distributed architectures, coupled with accelerated release demands. This paper explores Deep Learning (DL) as a transformative force in software testing, proposing advanced frameworks leveraging neural networks, reinforcement learning, and generative models to automate test case generation, predict high-risk areas, facilitate fault localization, and optimize test prioritization—significantly reducing manual effort and resource usage.

Key techniques include Convolutional Neural Networks (CNNs) for fault detection, Long Short-Term Memory (LSTM) networks for predictive test case creation, and autoencoders for anomaly detection, augmented by transfer learning and semi-supervised methods to overcome limited labeled datasets. Integrated seamlessly into CI/CD pipelines, these approaches deliver adaptive, self-optimizing testing strategies, prioritizing high-risk components dynamically.

Despite challenges like computational overhead and data requirements, advancements in AI, edge computing, and XAI promise a paradigm shift, enhancing efficiency, accuracy, and agility in quality assurance.

Keywords: Deep Learning, Software Testing, Test Case Generation, Fault Detection, Predictive Analytics, Automation, Quality Assurance, Optimization.

Paper Id: 231946

Published On: 2025-01-06

Published In: Volume 13, Issue 1, January-February 2025

Cite This: AI-Driven Software Testing - A Deep Learning Perspective - Chandra Shekhar Pareek - IJIRMPS Volume 13, Issue 1, January-February 2025.

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