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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Integrating temporal convolutional networks with metaheuristic optimization for accurate software defect prediction.

Ahmed Abdelaziz1,2, Alia Nabil Mahmoud1, Vitor Santos1

  • 1Nova Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa, Portugal.

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A new hybrid model combining Temporal Convolutional Networks (TCN) with Antlion Optimization (ALO) significantly improves software defect detection accuracy. This advanced deep learning approach outperforms existing methods, enhancing software quality.

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Deep learning is crucial for improving software quality by identifying defects.
  • Defect detection remains a significant challenge in the software development lifecycle.

Purpose of the Study:

  • To determine the most effective deep learning model for software defect detection.
  • To introduce and evaluate a novel hybrid model integrating Temporal Convolutional Networks (TCN) with Antlion Optimization (ALO).

Main Methods:

  • Proposed two models: a basic TCN and a hybrid TCN-ALO model.
  • Utilized Antlion Optimization (ALO) to optimize Temporal Convolutional Network (TCN) weights for defect detection.
  • Evaluated model performance using metrics like accuracy, sensitivity, specificity, and area under the curve.

Main Results:

  • The hybrid TCN-ALO model significantly outperformed the basic TCN across all performance metrics.
  • The hybrid model achieved higher accuracy (21.8%, 19.6%, 31.3%) than CNN, GRU, and BiLSTM.
  • The proposed model showed a 13.6% higher area under the curve compared to Deep Forest.

Conclusions:

  • The hybrid TCN-ALO model demonstrates superior effectiveness for accurate software defect detection.
  • This approach offers a promising solution for enhancing software quality through advanced defect identification.
  • The findings confirm the potential of optimized deep learning models in software engineering.