Prediction of Defects in Software Using Machine Learning Classifiers
In: Computational Methods and Data Engineering ISBN: 9789811579066; (2020-11-05)
Online
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Zugriff:
The Software Defect Prediction (SDP) model engages in predicting defects and bugs in the software. This model will detect and predict bugs during early stages of the software development life cycle to improve the overall quality of software and reduce the cost also. In this paper, the author presents a model that will predict the bugs with the help of machine learning classifiers. For this model, the researcher has used the dataset NASA from the known repositories and used two supervised ML classifier algorithms such as linear supervision (LR) and Naive Bayes (NB) for detecting and predicting faults. This study describes how ML algorithms work effectively in SDP models. The results collected showed that the linear regression approach performs better and predicts the faults with accuracy.
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Prediction of Defects in Software Using Machine Learning Classifiers
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Autor/in / Beteiligte Person: | Singh, Vijendra ; Arya, Ashima ; Kumar, Sanjay |
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Quelle: | Computational Methods and Data Engineering ISBN: 9789811579066; (2020-11-05) |
Veröffentlichung: | Springer Singapore, 2020 |
Medientyp: | unknown |
DOI: | 10.1007/978-981-15-7907-3_37 |
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