Published on

Automated Bug Detection in Software Using Machine Learning

Authors
  • avatar
    Name
    Project Mart
    Twitter

Introduction

Automated bug detection is a critical aspect of software development, aiming to identify and resolve defects early in the development cycle. This project proposal outlines a system that leverages machine learning to enhance the accuracy and efficiency of bug detection in software projects.

Background

Traditional methods for bug detection often rely on manual code reviews and static analysis tools, which can be time-consuming and error-prone. Recent research has demonstrated that machine learning techniques can significantly improve bug detection by analyzing code patterns and historical bug data. Machine learning models, such as supervised and unsupervised learning algorithms, have been effectively used to predict and classify software defects.

Project Objective

The primary objective of this project is to develop a robust bug detection system using machine learning algorithms. The system aims to outperform traditional methods by incorporating advanced feature extraction techniques and leveraging large-scale datasets of labeled code samples.

Methodology

1. Data Collection and Preprocessing

  • Datasets: Utilize publicly available datasets such as the BugNet dataset from GitHub for training and evaluation.
  • Data Cleaning: Remove unnecessary elements such as comments and whitespace, and normalize code formatting.
  • Feature Extraction: Extract features like code complexity metrics, frequency of specific keywords, and historical bug data.

2. Model Selection and Training

  • Supervised Learning Models: Implement models like Decision Trees and Support Vector Machines (SVM) for binary classification tasks.
  • Unsupervised Learning Models: Use clustering algorithms to identify new types of bugs not previously labeled.

3. Evaluation

  • Metrics: Evaluate model performance using accuracy, precision, recall, and F1-score.
  • Comparison: Compare the proposed model's performance with traditional bug detection methods to demonstrate improvements.

Expected Outcomes

The proposed system is expected to achieve higher accuracy in detecting software bugs compared to traditional methods. By utilizing machine learning techniques, the system should effectively handle variations in code patterns across different projects and environments.

Conclusion

This project aims to advance the field of automated bug detection by developing a state-of-the-art system capable of accurately identifying defects in software. The integration of machine learning models is anticipated to provide significant improvements in performance and efficiency.

For further details on related research, please refer to the paper "Automated Bug Detection Using Machine Learning Algorithms," available at https://ieeexplore.ieee.org/document/8768831.

Dataset link: BugNet Dataset on GitHub

Buy Project