Feature selection using a multi-strategy improved parrot optimization algorithm in software defect prediction.

Area of Science:

• Software Engineering
• Artificial Intelligence
• Machine Learning

Background:

• Software defect detection is crucial for improving software quality and reducing costs.
• Traditional methods face challenges with irrelevant features and suboptimal optimization.
• Existing optimization algorithms may suffer from premature convergence and limited global search.

Purpose of the Study:

• To develop a novel multi-strategy enhanced Parrot Optimization algorithm (MEPO) addressing limitations of the original PO algorithm.
• To introduce a binary MEPO (BMEPO) for effective feature selection in software defect prediction.
• To propose a heterogeneous data stacking ensemble learning algorithm (HEDSE) for enhanced defect prediction performance.

Main Methods:

• Development of a multi-strategy enhanced Parrot Optimization algorithm (MEPO).
• Application of a binary MEPO (BMEPO) for feature selection optimization.
• Implementation of a heterogeneous data stacking ensemble learning algorithm (HEDSE) for classification.

Main Results:

• MEPO demonstrates superior convergence speed and solution accuracy on benchmark functions.
• BMEPO shows enhanced competitiveness in feature selection quality and classification performance.
• HEDSE significantly outperforms existing methods on 16 open-source software defect datasets.

Conclusions:

• The proposed MEPO, BMEPO, and HEDSE offer a novel and effective solution for software defect prediction.
• These approaches hold significant practical value for improving software quality and reducing costs.
• The study provides a robust framework for optimizing defect detection in real-world software engineering.