Users Review’s on Software Defect Prediction Utilizing Machine Learning methods

نوع المستند : المقالة الأصلية

المؤلفون

1 قسم نظم المعلومات بكليه الحاسبات والذكاء الاصطناعي جامعه حلوان مصر

2 قسم برمجه الاله واسترجاع المعلومات بكليه الذكاء الاصطناعي جامعه كفرالشيخ مصر

3 قسم برمجه الاله واسترجاع المعلومات بكليه الذكاء الاصطناعي جامعة كفرالشيخ مصر

4 قسم هندسه الالكترونيات كليه الهندسه جامعه كفرالشيخ مصر

المستخلص

Software Defect Prediction (SDP) is a crucial and helpful method for upgrading software reliability and quality. It enables more effective project management by predicting potential release delays early on and facilitating cost-effective corrective actions to enhance software quality. This is achieved by forecasting which modules in a large software product are likely to have the highest number of defects in the next version. However, creating reliable defect forecasting models remains a challenging issue, leading to the presentation of numerous methods in literature. Typically, machine learning (ML) classifiers are employed, using manually designed attributes (like complexity measures) to identify problematic code. However, these attributes often fail to capture the full structural and semantic details of the software. Incorporating this information is crucial for the development of accurate defect prediction models. This study covers various defect prediction strategies and explores recent research on ML methodologies for SDP, aiming to bridge the gap between software semantics and defect forecasting attributes. By doing so, it seeks to produce more precise and accurate forecasting.
 

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