Title: Automated Code Review System using Machine Learning Techniques – Complete project material

Table of Contents

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

Chapter 2: Literature Review
2.1 Overview of Automated Code Review Systems
2.2 Machine Learning Techniques for Code Review
2.3 Related Studies on Automated Code Review Systems
2.4 Gaps in Existing Literature
2.5 Theoretical Framework

Chapter 3: System Design
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Machine Learning Model Selection
3.4 Integration of Automated Code Review System
3.5 User Interface Design

Chapter 4: Implementation
4.1 Data Collection and Preparation
4.2 Model Training and Validation
4.3 System Testing and Evaluation
4.4 Performance Metrics
4.5 Challenges Faced during Implementation

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Implications for Future Research
5.3 Recommendations for Practitioners
5.4 Conclusion

Project Summary:

Title: Automated Code Review System using Machine Learning Techniques

The increasing complexity and volume of code in software development projects have made manual code review processes time-consuming and error-prone. To address this issue, this final year project proposes the development of an Automated Code Review System using Machine Learning Techniques.

The project aims to automate the code review process using machine learning models trained on historical code quality data. By utilizing machine learning algorithms, the system will be able to identify code quality issues, suggest improvements, and prioritize code review tasks based on the severity of issues detected.

The project involves a thorough literature review on automated code review systems and machine learning techniques for code analysis. The system design includes the architecture, data collection, preprocessing, machine learning model selection, and user interface design. The implementation phase focuses on data collection, model training, testing, and evaluation.

The project contributes to the field of software engineering by providing a more efficient and accurate solution for code review processes. The findings of the study will help practitioners in improving code quality and reducing the time and effort involved in manual code reviews.

In conclusion, the Automated Code Review System using Machine Learning Techniques has the potential to revolutionize the code review process in software development projects. Further research and development in this area can lead to more advanced and intelligent automated code review systems in the future.

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