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SynergyBug: A deep learning approach to autonomous debugging and code remediation.

Hong Chen1

  • 1School of Information Engineering, JingDeZhen Ceramic University, JingDeZhen, 333403, JiangXi, China. chanh0601@163.com.

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Summary
This summary is machine-generated.

SynergyBug, an automated system using BERT and GPT-3, efficiently detects and repairs software bugs, significantly improving software quality and reducing manual debugging efforts.

Keywords:
Automated debuggingBug detectionGPT-BERT hybrid modelProactive bug managementSoftware quality

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Area of Science:

  • Software Engineering
  • Artificial Intelligence

Background:

  • Manual bug detection and resolution are inefficient for complex software.
  • Traditional static methods struggle with modern software complexity.

Purpose of the Study:

  • To develop an automated system for autonomous bug detection and repair.
  • To minimize human involvement in the debugging process.
  • To enhance software quality, reliability, and performance.

Main Methods:

  • SynergyBug combines BERT for contextual embedding generation from bug reports, logs, and documentation.
  • GPT-3 utilizes these embeddings to generate code fixes and explanations.
  • A unified process integrates detection and resolution for continuous debugging.

Main Results:

  • Achieved 98.79% accuracy, 97.23% precision, and 96.56% recall, outperforming conventional methods.
  • Demonstrated high detection rates for functional (94%), performance (90%), and security (92%) bugs.
  • Scalable to over 100,000 bug reports without performance degradation.

Conclusions:

  • SynergyBug offers a revolutionary approach to proactive bug management.
  • The system enhances debugging speed and improves the overall software development lifecycle.
  • Represents a significant advancement towards automated debugging tools for operational safety.