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Table of Contents

Chapter 1: Introduction
1.1 Background of the Study
1.2 Problem Statement
1.3 Objectives of the Study
1.4 Research Questions
1.5 Significance of the Study
1.6 Scope of the Study
1.7 Limitations of the Study

Chapter 2: Literature Review
2.1 Introduction to Code Review Systems
2.2 Machine Learning Algorithms in Code Analysis
2.3 Importance of Automated Code Review
2.4 Existing Tools and Systems
2.5 Challenges and Opportunities in Code Review

Chapter 3: System Design
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Machine Learning Model Design
3.4 User Interface Design
3.5 Integration with Version Control Platforms

Chapter 4: Implementation
4.1 Dataset Collection and Preparation
4.2 Model Training and Evaluation
4.3 User Interface Development
4.4 Integration with Version Control Platforms
4.5 Testing and Validation

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Contributions of the Study
5.4 Recommendations for Future Work

Project Summary:

The project aims to develop an automated code review system that leverages machine learning algorithms to provide feedback on code quality. This system will be able to detect potential bugs, inefficiencies, and adherence to coding standards, ultimately improving the overall quality of the code base. The research involved designing and implementing a machine learning model trained on a dataset of code snippets and their corresponding reviews.

The system will include a user interface for developers to submit their code for review and view the feedback generated by the system. Additionally, the project will explore the integration of the system with popular version control platforms such as GitHub for a seamless code review workflow.

The literature review highlighted the importance of code review systems and the use of machine learning algorithms in code analysis. Existing tools and systems were also explored, along with the challenges and opportunities in code review.

The system design chapter outlined the architecture, data collection, preprocessing, machine learning model design, user interface design, and integration with version control platforms. The implementation chapter discussed dataset collection, model training, user interface development, integration with version control platforms, testing, and validation.

In conclusion, the project aims to contribute to the field of automated code review by developing a system that can efficiently analyze code quality and provide valuable feedback to developers. Recommendations for future work include enhancing the machine learning model’s accuracy, expanding the dataset, and integrating more functionalities with version control platforms.

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