AI Enabled Cloud RAN Test Automation : Automatic Test Case Prediction Using Natural Language Processing and Machine Learning Techniques

Sammanfattning: The Cloud Radio Access Network (RAN) is a technology used in the telecommunications industry. It provides a flexible, scalable, and costeffective solution for managing and delivering seamless wireless network services. However, the testing of Cloud RAN applications poses formidable challenges due to its complex nature, resulting in potential delays in product delivery and amplified costs. Using the power of test automation is an approach to tackling these challenges. By automating the testing process, we can reduce manual efforts, enhance the accuracy and efficiency of testing procedures, and ultimately expedite the delivery of high-quality products. In this era of cutting-edge advancements, artificial intelligence (AI) and machine learning (ML) can be used to aid Cloud RAN testing. These technologies empower us to swiftly identify and address complex issues. The goal of this thesis is to have a data-driven approach toward Cloud RAN test automation. Machine learning along with natural language processing techniques are used to automatically predict test cases from test instructions. The test instructions are analyzed and keywords are extracted from them using natural language processing techniques. The performance of two keyword extraction techniques is compared. SpaCy was the best-performing keyword extractor. Test script prediction from these keywords is done using two approaches; using test script names and using test script contents. Random Forest was the best performing model for both these approaches when the data were oversampled and when it was undersampled as well.