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Revolutionizing Software Debugging: GA-TCN Approach for Advanced Bug Training

EasyChair Preprint no. 12810

7 pagesDate: March 28, 2024


Software debugging is a critical process in software development, often requiring significant time and resources to identify and rectify bugs. Traditional debugging methods face challenges in handling complex bugs efficiently. This paper introduces a revolutionary approach to software debugging utilizing Genetic Algorithm (GA) and Temporal Convolutional Networks (TCN). The GA-TCN framework combines the evolutionary search capability of genetic algorithms with the temporal analysis power of TCNs to enhance bug detection and resolution. Through iterative evolution and temporal pattern recognition, GA-TCN dynamically learns from historical debugging data to improve bug identification accuracy and speed up the debugging process. Experimental results demonstrate the effectiveness of the GA-TCN approach in detecting and addressing various types of bugs, outperforming traditional debugging methods significantly. This innovative approach has the potential to revolutionize software debugging practices, enabling developers to streamline the debugging process and deliver more robust and reliable software products.

Keyphrases: Bug Training, evolutionary search, Genetic Algorithm, Software Debugging, temporal analysis, Temporal Convolutional Networks

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Roni Joni},
  title = {Revolutionizing Software Debugging: GA-TCN Approach for Advanced Bug Training},
  howpublished = {EasyChair Preprint no. 12810},

  year = {EasyChair, 2024}}
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