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Enhancing Software Bug Training with GA-TCN: A Revolutionary Approach

EasyChair Preprint no. 12804

6 pagesDate: March 28, 2024


This paper introduces a groundbreaking approach to software bug training utilizing a Genetic Algorithm and Time Convolution Neural Network (GA-TCN). By combining the evolutionary principles of genetic algorithms with the temporal learning capabilities of TCNs, we present a novel method for identifying and addressing software bugs efficiently. Our approach leverages the power of genetic algorithms to evolve optimal solutions while harnessing TCN's ability to capture long-term dependencies in bug patterns over time. Experimental results demonstrate the effectiveness and superiority of GA-TCN in software bug training compared to traditional methods, showcasing its potential to revolutionize bug detection and resolution practices in software engineering. The experimental results showcase promising advancements in the field, indicating the potential for a paradigm shift in the way software bugs are addressed and mitigated.

Keyphrases: bug detection, Evolutionary Computing, GA-TCN, Genetic Algorithm, Optimization, Software bug training, Software Engineering, Temporal Learning, Time Convolution Neural Network

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Asad Ali},
  title = {Enhancing Software Bug Training with GA-TCN: A Revolutionary Approach},
  howpublished = {EasyChair Preprint no. 12804},

  year = {EasyChair, 2024}}
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