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Symmetric bug prediction in software requirement by machine learning algorithms.

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Machine Learning (ML) models accurately predict software bug resolution times. The proposed Random Forest, Artificial Neural Network, and Adaptive Moment Estimation approach achieved 98% accuracy, significantly improving defect prediction in software development.

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Data Science

Background:

  • Software development faces challenges with unpredictable failures and the need for timely defect correction.
  • Early defect identification and resolution are crucial for enhancing software performance, accuracy, durability, and reliability.
  • Machine Learning (ML) offers potential for analyzing Software Requirements (SR) to accelerate development and defect correction.

Purpose of the Study:

  • To forecast bug resolution times using ML models.
  • To compare the accuracy of different ML algorithms in predicting defect resolution and feature completion times.
  • To improve the precision and accuracy of software failure time prediction.

Main Methods:

  • Utilized classification and regression-based ML models, including Random Forest (RF), Artificial Neural Network (ANN), and Adaptive Moment Estimation (AME).
  • Applied models to predict bug resolving times using software development lifecycle data (issue detection, testing, validation).
  • Evaluated model performance against the K-Nearest Neighbors (KNN) algorithm.

Main Results:

  • The proposed RF, ANN, and AME models achieved an accuracy of 98%.
  • The K-Nearest Neighbors (KNN) algorithm showed a lower accuracy of 66%.
  • The dataset with symmetric attributes exhibited clear trends and strong correlations, supporting the ML models' effectiveness.

Conclusions:

  • The developed ML approach significantly outperforms existing methods for software failure time prediction.
  • The proposed model demonstrates high precision and accuracy in predicting defect resolution and feature completion times.
  • This research provides a robust solution for defect prediction and resolution in Software Requirements (SR).