Enhanced Software Defect Prediction Through Homogeneous Ensemble Models
Keywords:
effective, methods, Boosting, accelerating, isolatingAbstract
The authors of this paper recommend employing state-of-the-art Machine Learning methods for fault prediction in computer programmes. The Promise software engineering repository, where NASA stores its data, serves as an example. The basic objective of software defect prediction is the early discovery of software flaws. Machine learning algorithms can help with this by making predictions based on historical data. The initial experiments' results suffered from low precision and recall since they relied on outdated machine learning methods. Modern machine learning methods such as Naive Bayes, Boosting, and Grid Search were incorporated to increase the model's accuracy. The performance of the software defect prediction model has been greatly enhanced through the use of state-of-the-art machine learning techniques. The precision and recall rates, two measures of how well a system can forecast errors, have also grown. Naive Bayes, Boosting, and Grid Search are just a few of the modern machine learning methods that helped improve the software defect prediction model. The algorithms' increased accuracy and recall rates show how effective they are at finding and predicting software defects. The importance of using state-of-the-art machine learning methods to the task of defect prediction is emphasised. Using techniques such as Naive Bayes, Boosting, and Grid Search can significantly increase the model's effectiveness. These methods have improved software development processes by accelerating and better isolating bugs.
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