[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127494-en":3,"doc-seo-127494-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127494,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning Approach to 5G Layer 1 Code Review - Master’s Thesis","Automatic code review becomes essential as software systems grow to massive scale and underpin precise, delicate, and expensive operations such as space missions. Manual verification cannot reliably catch every typo, bug, or security issue hidden within billions of lines of code, motivating robust deep-learning based bug finding methods. This thesis rebuilds the neural network deepreview model, tests modified architectures and alternative feature extraction, and evaluates models on Nokia’s 5G Layer 1 implementation dataset.","Machine Learning Approach to 5G Layer 1 Code Review  \nMichał Porębski  \nSchool of Electrical Engineering  \nThesis submitted for examination for the degree of Master of Science in Technology.  \nEspoo 30.11.2022  \nSupervisor  \nProf. Esa Kallio  \nAdvisor  \nMSc Marko Tuononen  \nCopyright © 2022 Michał Porębski  \nAalto University, P.O. BOX 11000, 00076 AALTO [www.aalto.fi](www.aalto.fi)  \nAbstract of the master’s thesis  \n\n| Author Michał Porębski |\n| --- |\n| Title Machine Learning Approach to 5G Layer 1 Code Review |\n| Degree programme Electronics and electrical engineering |\n| Major Space Science and Technology Code of major ELEC3039 |\n| Supervisor Prof. Esa Kallio |\n| Advisor MSc Marko Tuononen |\n| Date 30.11.2022 Number of pages 54 Language English |\n| Abstract\u003Cbr>The programming is used in most of the industries and domains of life. Programming projects are becoming bigger and bigger, with millions of developers working on them across the world. Such projects are sometimes the core of precise, delicate and expensive operations, like space missions. They often require autonomous work for many years, therefore they have to be thoroughly tested before the exploitation. Hence, each change which is done in such project needs to be verified by automatic system and other programmers. It is not a trivial task, because a typo, a bug, a security violation, etc. easily appear in the billions of lines of code. Such mistakes need to be found and fixed, otherwise the consequences can be devastating. For that purpose, many automatic bug finding approaches are being researched. The deep neural networks are the most promising solutions. They allow for checking the issues which were caught only by other programmers and not by already existing automatic systems.\u003Cbr>This work focuses on machine learning approach to code review and software quality assurance. It describes the recreation of neural network deepreview model and experiments with its modifications. It also proposes a different approach to a feature extraction phase. The thesis consists of descriptions of created architectures, shows results of the experiments and compares them with original article. The implemented models are tested on the database gathered from specific branch of Nokia Corporation responsible for implementation of 5G layer 1 . It is described how such data are processed and analysed. It also provides a short history of the evolution of such automatic systems for code review. |\n| Keywords machine learning, neural networks, software quality assurance, code review |\n\niv  \nPreface  \nI would like to express my sincere gratitude to my advisor, Marko Tuononen, for providing the good and stubborn guidance through the whole project and for having the patience for my slow problem-solving.  \nI want to extend my appreciation to Professor Esa Kallio for having the patience for me and good guidance along the way.  \nI would like to thank my fiancé, Klaudii for keeping me going against many adversities and always being there for me when I needed it.  \nI highly appreciate the support of my parents, brother, his wife and my friends. The time and attention they gave me were priceless.  \nI want to thank my team members and my manager, Topi Rantalainen for listening to my stuttering during daily meetings.  \nAt the end, I would also like to thank my flatmate, Jakubowi Cisło, for having constant doubts that I will ever finish this thesis.  \nThe thesis was ordered and supervised by Nokia Corporation.  \nEspoo, 30 .11.2022 Michał Porębski  \nv  \nContents  \nAbstract iii  \nPreface iv  \nContents v  \nSymbols and abbreviations vii  \n1 Introduction 1  \n2 Theoretical introduction 3  \n2. 1 Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3  \n2.1.1 Dense Neural Network Layer . . . . . . . . . . . . . . . . . . . 4  \n2.1.2 Convolutional Neural Network Layer .............. 4  \n2.1.3 Long Short-Term Memory Neural Network Layer ....... 5  \n2.1.4 Attention Neural Network Layer .....","cbCaiegK6i3kcgYK","https://ap.wps.com/l/cbCaiegK6i3kcgYK","pdf",1775611,1,60,"English","en",105,"# Introduction\n# Theoretical introduction\n## Neural Network\n## Loss function\n## Validation\n## Metrics\n# Automatic code review methods\n# Principle of operation\n## Data gathering\n## Data processing\n## Model construction\n## Model training\n## Model evaluation","[{\"question\":\"What problem does the thesis address in software development?\",\"answer\":\"It addresses the difficulty of verifying code changes in large, safety-critical software where typos, bugs, or security violations can be costly and hard to detect manually.\"},{\"question\":\"What core model and techniques does the thesis study?\",\"answer\":\"It recreates the neural network deepreview model, experiments with its modifications, and proposes a different feature extraction approach.\"},{\"question\":\"How are the proposed machine learning models evaluated?\",\"answer\":\"The implemented models are tested on a dataset gathered from Nokia’s branch responsible for implementing 5G Layer 1, including description of data processing and analysis.\"}]","Machine Learning Approach to 5G Layer 1 Code Review - 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