Design and Implementation of a Machine Learning-Based Code Smell Detection Tool for Improving Code Quality

1. Introduction 1.1 Background and Motivation 1.2 Problem Statement 1.3 Objectives of the Study 1.4 Structure of the Thesis 2. Code Quality and Code Smells 2.1 Definition of Code Quality 2.2 Overview of Code Smells 2.3 Common Types of Code Smells 2.4 Impact of Code Smells on Software 3. Machine Learning in Software Engineering 3.1 Introduction to Machine Learning 3.2 Applications in Software Engineering 3.3 Advantages of Machine Learning 3.4 Challenges in Machine Learning Implementation 4. Related Work 4.1 Code Smell Detection Tools 4.2 Machine Learning Approaches 4.3 Evaluation of Existing Solutions 4.4 Identified Gaps in Research 5. Design of the Detection Tool 5.1 System Architecture Overview 5.2 Machine Learning Model Selection 5.3 Data Collection and Preprocessing 5.4 Feature Selection and Engineering 6. Implementation and Development 6.1 Development Environment and Tools 6.2 Integration with Codebases 6.3 User Interface Design 6.4 Testing and Validation 7. Case Study and Evaluation 7.1 Case Study Methodology 7.2 Evaluation Metrics 7.3 Results and Analysis 7.4 Discussion of Findings 8. Conclusion and Future Work 8.1 Summary of Contributions 8.2 Limitations of the Study 8.3 Recommendations for Future Research 8.4 Potential Improvements to the Tool