Automated Bug Detection in Software Development using Machine Learning Techniques

Thesis Abstract

**Abstract
** Automated bug detection in software development using machine learning techniques is a crucial area of research that aims to enhance the quality and reliability of software systems. This project focuses on leveraging machine learning algorithms to automatically identify and classify bugs in software code, thereby improving the efficiency and accuracy of bug detection processes. The significance of this research lies in its potential to streamline software development workflows, reduce debugging efforts, and ultimately deliver more robust and error-free software products to end-users. The thesis begins with an introduction that provides an overview of the research topic, followed by a background of the study that highlights the importance of automated bug detection in software development. The problem statement outlines the challenges faced in traditional bug detection methods, leading to the objective of the study, which is to develop a machine learning-based approach for automated bug detection. The limitations and scope of the study are also discussed, along with the significance of the research in advancing the field of software engineering. The structure of the thesis is outlined to guide the reader through the subsequent chapters. Chapter two presents a comprehensive literature review, covering ten key research studies and developments in the field of automated bug detection and machine learning techniques. This review provides a foundation for understanding the current state-of-the-art and identifying gaps in existing research that this project aims to address. Chapter three details the research methodology employed in this study, including data collection, feature extraction, model selection, and evaluation metrics for assessing the performance of the bug detection system. The methodology section also discusses the experimental setup and validation procedures to ensure the reliability and validity of the research findings. Chapter four presents an elaborate discussion of the findings obtained from the experiments conducted in this research. The results of the bug detection system are analyzed, and the performance metrics are compared with existing approaches to evaluate the effectiveness of the proposed machine learning techniques. This chapter also includes a detailed interpretation of the results and discusses the implications for future research and practical applications. Finally, chapter five offers a conclusion and summary of the project thesis, summarizing the key findings, contributions, and implications of the research. The conclusion also highlights the significance of the study in advancing the field of automated bug detection in software development and suggests potential avenues for further research and development in this area. In conclusion, this thesis on automated bug detection in software development using machine learning techniques provides a comprehensive investigation into the application of advanced algorithms for improving bug detection processes. The research findings contribute to enhancing software quality, reducing development time, and increasing the overall efficiency of software development practices. This project serves as a valuable resource for researchers, practitioners, and software developers seeking to leverage machine learning technologies for more effective bug detection in software engineering.