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Efficient Technological Evaluation and Bug Training via GA-TCN Framework

EasyChair Preprint no. 12814

6 pagesDate: March 28, 2024


Efficient evaluation of technological systems and robust bug training are crucial aspects in software development and maintenance. In this study, we propose a novel framework, named GA-TCN (Genetic Algorithm and Time Convolutional Neural Network), for addressing these challenges. GA-TCN integrates the genetic algorithm (GA) for optimization and the time convolutional neural network (TCN) for effective bug detection and training. The GA component optimizes the parameters of the TCN model, enhancing its performance in identifying and addressing software bugs. Through a series of experiments and evaluations on real-world datasets, we demonstrate the efficacy of the GA-TCN framework in improving technological evaluation and bug training processes. Our results indicate significant enhancements in bug detection accuracy and training efficiency compared to traditional methods. Moreover, the proposed framework exhibits scalability and adaptability, making it suitable for various software development environments.

Keyphrases: bug detection, Bug Training, Genetic Algorithm, Optimization, software development, Technological evaluation, Time Convolutional Neural Network

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Haney Zaki},
  title = {Efficient Technological Evaluation and Bug Training via GA-TCN Framework},
  howpublished = {EasyChair Preprint no. 12814},

  year = {EasyChair, 2024}}
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