Early Software Defects Density Prediction: Training the International Software Benchmarking Cross Projects Data Using Supervised Learning

Recent reviews of the literature indicate the need for empirical studies on cross-project defect prediction (CPDP) that would allow aggregation of the evidence and improve predictive performance. Most empirical studies predict defects at granularity levels of method, class, file, and module/package...

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Veröffentlicht in:	IEEE access 2023, Vol.11, p.141965-141986
Hauptverfasser:	Tahir, Touseef, Gencel, Cigdem, Rasool, Ghulam, Umer, Tariq, Rasheed, Jawad, Yeo, Sook Fern, Cevik, Taner
Format:	Artikel
Sprache:	eng
Schlagworte:	Benchmarks Classification Codes Costs Cross projects dataset Datasets defect prediction Defects Density Fault detection fault prediction feature selection ISBSG dataset Literature reviews Machine learning Multilayers Neural networks Performance prediction Prediction methods Prediction models Predictive models Project management Regression Root-mean-square errors Size measurement Software Software engineering Standard scores Supervised learning Training Transformations
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