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Introduction

Automated code review using machine learning is a process that involves the use of machine learning algorithms to automatically analyze and review software code. This process can help developers improve the quality of their code, identify potential issues early on, and ultimately save time and resources. As the complexity of software systems continues to grow, manual code review processes are becoming increasingly inefficient and error-prone. Machine learning offers a promising solution to this problem by leveraging algorithms that can learn from past code reviews and provide valuable insights into the quality of code.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of code review processes
2.2 Benefits of automated code review
2.3 Challenges of automated code review
2.4 Machine learning in software development
2.5 Existing machine learning models for code review
2.6 Comparison of different machine learning algorithms
2.7 Case studies of automated code review implementations
2.8 Best practices for automated code review
2.9 Future trends in automated code review
2.10 Summary of key findings in literature review

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data preprocessing techniques
3.4 Feature selection and extraction
3.5 Machine learning model selection
3.6 Model training and evaluation
3.7 Performance metrics
3.8 Ethical considerations
3.9 Limitations of methodology

Chapter 4: Discussion of Findings
4.1 Analysis of results
4.2 Comparison of machine learning models
4.3 Interpretation of performance metrics
4.4 Implications for software development
4.5 Practical implications for developers
4.6 Recommendations for future research
4.7 Limitations of the study
4.8 Areas for further exploration

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Practical implications for industry
5.4 Recommendations for developers
5.5 Conclusion and implications for future research

Thesis Overview: Automated Code Review Using Machine Learning

The growing complexity of software systems has made manual code review processes increasingly inefficient and error-prone. Automated code review using machine learning offers a promising solution to this challenge by leveraging algorithms that can automatically analyze and review software code. This thesis explores the potential benefits of using machine learning for code review and presents a comprehensive analysis of existing literature, methodologies, and findings in the field.

The introduction provides a background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. The literature review delves into the overview of code review processes, benefits, challenges, machine learning in software development, existing models, comparisons of algorithms, case studies, best practices, and future trends. The research methodology details the design, data collection, preprocessing, feature selection, model selection, training, evaluation, metrics, ethical considerations, and limitations.

The discussion of findings analyzes results, compares models, interprets metrics, discusses implications for software development, provides practical recommendations, and suggests areas for future research. The conclusion and summary recap key findings, contributions to the field, practical implications for industry, recommendations for developers, and implications for future research. This thesis aims to provide valuable insights and recommendations for developers looking to improve the quality of their code through automated code review using machine learning.

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