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

EasyChair Preprint 12804

Abstract

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

@booklet{EasyChair:12804,
  author    = {Asad Ali},
  title     = {Enhancing Software Bug Training with GA-TCN: A Revolutionary Approach},
  howpublished = {EasyChair Preprint 12804},
  year      = {EasyChair, 2024}}