The Cloud Radio Access Network (RAN) is a groundbreaking technology employed in the telecommunications industry, offering flexible, scalable, and cost-effective solutions for seamless wireless network services. However, testing Cloud RAN applications presents significant challenges due to their complexity, potentially leading to delays and increased costs. A paramount solution to overcome these obstacles is test automation. Automating the testing process not only dramatically reduces manual efforts but also enhances testing accuracy and efficiency, expediting the delivery of high-quality products. In the current era of cutting-edge advancements, artificial intelligence (AI) and machine learning (ML) play a transformative role in revolutionizing Cloud RAN testing. These innovative technologies enable rapid identification and resolution of complex issues, surpassing traditional methods. The objective of this thesis is to adopt an AI-enabled approach to Cloud RAN test automation, harnessing the potential of machine learning and natural language processing (NLP) techniques to automatically select test cases from test instructions. Through thorough analysis, relevant keywords are extracted from the test instructions using advanced NLP techniques. The performance of three keyword extraction methods is compared, with SpaCy proving to be the superior keyword extractor. Using the extracted keywords, test script prediction is conducted through two distinct approaches: using test script names and using test script contents. In both cases, Random Forest emerges as the top-performing model, showcasing its effectiveness with diverse datasets, regardless of oversampling or undersampling data augmentation techniques. Based on the rule-based approach, the predicted test scripts are utilized to determine the order of execution among the predicted test scripts. The research findings highlight the significant impact of AI and ML techniques in streamlining test case selection and automation for Cloud RAN applications. The proposed AI-enabled approach optimizes the testing process, resulting in faster product delivery, reduced manual workload, and overall cost savings.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:su-223665 |
| Date | January 2023 |
| Creators | Gupta, Alok |
| Publisher | Stockholms universitet, Institutionen för data- och systemvetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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