Improving Software Defects Detection: Machine Learning Methods and Static Analysis Tools

 

Project Synopsis

Title: Improving Software Defects Detection: Machine Learning Methods and Static Analysis Tools

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1. Introduction

Software defects are among the most critical challenges in modern software development, leading to increased maintenance costs, reduced reliability, and potential system failures. Traditional testing and debugging techniques often fail to capture subtle and complex defects early in the development cycle. To address these challenges, this project proposes an integrated framework that leverages machine learning (ML) models alongside static analysis tools to improve software defect detection accuracy and efficiency.

 

2. Problem Statement

Existing defect detection techniques primarily rely on manual testing or conventional automated tools, which:

• May generate a high number of false positives/negatives.
• Struggle with large-scale software systems with millions of lines of code.
• Lack adaptability to evolving coding patterns and practices.

Thus, there is a need for a hybrid approach that combines static analysis tools with machine learning methods to reduce false alarms, detect hidden patterns, and enhance early defect identification.

 

3. Objectives

• To apply machine learning models (e.g., Decision Trees, Random Forest, SVM, Deep Learning) for predicting software defects using historical code metrics and defect data.
• To integrate static code analysis tools (e.g., SonarQube, FindBugs, PMD, Clang Static Analyzer) for identifying common coding errors and vulnerabilities.
• To design a hybrid framework combining ML predictions and static analysis insights for improved defect detection.
• To evaluate the framework based on accuracy, precision, recall, and F1-score against conventional methods.
• To reduce software maintenance costs and improve code quality.

 

4. Proposed Approach

1. Data Collection:
• Gather open-source project datasets (e.g., PROMISE, NASA MDP, GitHub repositories) with historical defect labels.
• Extract software metrics (LOC, complexity, dependencies, churn rate).
2. Static Analysis:
• Run static analyzers to detect coding flaws, vulnerabilities, and maintainability issues.
• Generate rule-based defect reports.
3. Machine Learning Model:
• Train ML algorithms on defect-labeled data to identify defect-prone modules.
• Apply feature engineering to combine code metrics + static analysis results.
4. Hybrid Framework:
• Integrate ML predictions with static analysis outputs.
• Implement ensemble techniques to reduce false positives.
5. Evaluation:
• Compare results with standalone static analysis tools and ML-only approaches.
• Use performance metrics (Accuracy, Precision, Recall, F1-Score, ROC-AUC).

 

5. Expected Outcomes

• A hybrid defect detection system combining ML and static analysis.
• Higher accuracy and lower false positives compared to existing methods.
• Better identification of critical defects and vulnerabilities early in the software lifecycle.
• Contribution toward improving software reliability, maintainability, and security.

 

6. Tools & Technologies

• Programming Languages: Python, Java, C/C++ (for dataset and tool integration)
• Machine Learning Frameworks: Scikit-learn, TensorFlow, PyTorch
• Static Analysis Tools: SonarQube, FindBugs, PMD, Clang Static Analyzer
• Datasets: PROMISE, NASA MDP, Open-source project repositories
• IDE & Environment: VS Code, Eclipse, Jupyter Notebook

 

7. Applications

• Large-scale enterprise software systems (banking, healthcare, e-commerce).
• Open-source project quality assurance.
• Safety-critical domains (automotive, aerospace, medical devices).
• Secure software development lifecycle (SSDLC).

 

8. Conclusion

This project aims to enhance software defect detection by leveraging the strengths of both machine learning models and static analysis tools. The proposed framework not only improves detection accuracy but also reduces false positives, leading to more reliable, secure, and maintainable software systems.