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A novel quality prediction model for component based software system using ACO-NM optimized extreme learning machine.

Kavita Sheoran1, Pradeep Tomar1, Rajesh Mishra1

  • 1Department of Computer Science and Engineering, Maharaja surajmal Institute of technology, Gautam Buddha University, Noida, Uttar Pradesh India.

Cognitive Neurodynamics
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Summary
This summary is machine-generated.

This study introduces an Extreme Learning Machine (ELM) classifier enhanced with Ant Colony Optimization and Nelder-Mead (ACO-NM) for predicting component quality in software engineering. The approach improves prediction accuracy for complex embedded systems.

Keywords:
Ant colony optimization (ACO)Extreme learning machine (ELM)Nelder–Mead (NM)Prediction qualitySoft computing

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Component-based software engineering (CBSE) is crucial for complex embedded systems.
  • Engineers face numerous quality requirements (safety, security, performance, etc.), making development challenging.
  • Accurate quality prediction is vital for enhancing CBSE systems.

Purpose of the Study:

  • To enhance quality prediction in component-based software engineering systems.
  • To propose a novel soft computing approach for component quality prediction.
  • To improve the development of complex embedded systems through better quality assessment.

Main Methods:

  • Utilized an Extreme Learning Machine (ELM) classifier.
  • Integrated Ant Colony Optimization and Nelder-Mead (ACO-NM) for ELM weight updates.
  • Focused on maintainability, independence, and portability as core quality metrics.

Main Results:

  • The proposed ACO-NM enhanced ELM demonstrated improved performance in quality prediction.
  • Key performance indicators such as Sensitivity, Precision, Specificity, and Accuracy were enhanced.
  • The method showed a significant rate of convergence and improved predictive metrics.

Conclusions:

  • The ACO-NM soft computing approach effectively enhances ELM for component quality prediction.
  • This method offers a robust solution for the quality challenges in complex embedded systems.
  • The findings support the use of advanced soft computing techniques for improving software quality assessment.