Titlebook: Intelligent Software Defect Prediction; Xiao-Yuan Jing,Haowen Chen,Baowen Xu Book 2023 The Editor(s) (if applicable) and The Author(s), un

使作嘔

書目名稱Intelligent Software Defect Prediction影響因子(影響力)




書目名稱Intelligent Software Defect Prediction影響因子(影響力)學(xué)科排名




書目名稱Intelligent Software Defect Prediction網(wǎng)絡(luò)公開度




書目名稱Intelligent Software Defect Prediction網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Intelligent Software Defect Prediction被引頻次




書目名稱Intelligent Software Defect Prediction被引頻次學(xué)科排名




書目名稱Intelligent Software Defect Prediction年度引用




書目名稱Intelligent Software Defect Prediction年度引用學(xué)科排名




書目名稱Intelligent Software Defect Prediction讀者反饋




書目名稱Intelligent Software Defect Prediction讀者反饋學(xué)科排名

DEBT

Xiao-Yuan Jing,Haowen Chen,Baowen XuProvides a comprehensive introduction to the current state of SDP research.Introduces a range of machine-learning-based SDP approaches proposed for different scenarios.Provides valuable insights and l

ATP861

軟膏

978-981-99-2844-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

Immunization

Machine Learning Techniques for Intelligent SDP,In this chapter, several common learning algorithms and their applications in software defect prediction are briefly introduced, including deep learning, transfer learning, dictionary learning, semi-supervised learning, and multi-view learning.

自負(fù)的人

殺蟲劑

Book 2023oftware defects usually produce incorrect or unexpected results and behaviors. Accordingly, software defect prediction (SDP) is one of the most active research fields in software engineering and plays an important role in software quality assurance. Based on the results of SDP analyses, developers c

Mettle

defenses

Introduction,he effective allocation or prioritization of quality assurance effort (test effort and code inspection effort). Construction of these prediction models are mostly dependent on historical or previous software project data referred to as a dataset.

SYN

Within-Project Defect Prediction,y learning (CDDL) approach for software defect classification and prediction. The widely used datasets from NASA projects are employed as test data to evaluate the performance of all compared methods. Experimental results show that CDDL outperforms several representative state-of-the-art defect prediction methods.