Journal of Autonomous Intelligence

Advances in Artificial Intelligence: Challenges and Opportunities

Submission deadline: 2024-03-31
Special Issue Editors

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has revolutionized the way we live and work, and its impact on society is growing rapidly. With advances in machine learning, deep learning, and natural language processing, AI has become a powerful tool for solving complex problems in various domains. However, the development and deployment of AI also present significant challenges and ethical concerns, including biases, transparency, privacy, and accountability. This special issue aims to explore the challenges and opportunities of AI and its impact on society. It includes articles that address various aspects of AI, such as its applications, limitations, and ethical implications. The issue also discusses the future of AI and its potential to transform society, including healthcare, education, and business.

Dr. Pawan Whig

Dr. Pavika Sharma,

Guest Editors


Artificial Intelligence; Machine Learning; Deep Learning; Natural Language Processing; Computer Vision; Autonomous Systems; Robotics; Ethics; Bias; Transparency; Privacy; Accountability; Applications

Published Paper

Software testing is an important aspect of software development to ensure the quality and reliability of the software. With the increasing complexity of software systems, the number of test cases has also increased significantly, making it challenging to execute all the test cases in a limited amount of time. Test case prioritization techniques have been proposedto tackle this problem by identifying and executing the most important test cases first. In this research paper, we propose the use of machine learning algorithms for prioritization of test cases. We explore different machine learning algorithms, including  decision  trees,  random  forests,  and  neural  networks,  and  compare  their  performance  with  traditional prioritization techniques such as code coverage-based and risk-based prioritization. We evaluate the effectiveness of these algorithms on various datasets and metrics such as the number of test cases executed, the fault detection rate, and the execution time. Our experimental results demonstrate that machine learning algorithms can effectively prioritize test cases and outperform traditional techniques in terms of reducing the number oftest cases executed while maintaining high fault detection rates. Furthermore, we discuss the potential limitations and future research directions of using machine learning algorithms for test case prioritization. Our research findings contribute to the development of more efficient and effective software testing techniques that can improve the quality and reliability of software systems