A Summary of Software Defect Model

Han, Wanjiang; Jiang, Heyang; Li, Weijian; Li, Ye

doi:10.1109/ca.2014.22

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…На думку авторів статті, у рамках дослідження дефектів ПЗ доцільно більш детально дослідити сам дефект ПЗ і описати його життєвий цикл в рамках процесу розробки ПЗ. Існуючі роботи в повній мірі не розглядають детально дефект ПЗ [5][6], у них взагалі не досліджені або не розглянуті не в повній мірі причиннонаслідкові зв'язки появи дефектів ПЗ [7][8], життєвий цикл (ЖЦ) дефекту ПЗ, а також його прив'язка до життєвого циклу розробки ПЗ [9][10]. У зв'язку з цим метою статті є розробка моделі життєвого циклу дефекту ПЗ і його проекція на ЖЦ розробки ПЗ.…”

Section: модель життєвого циклу дефекту програмного забезпеченняunclassified

Life Cycle Model of Software Defect

Gordieiev¹,

Leontiev²,

Radiy³

2020

mcm-tech

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Section: модель життєвого циклу дефекту програмного забезпеченняunclassified

Life Cycle Model of Software Defect

Gordieiev¹,

Leontiev²,

Radiy³

2020

mcm-tech

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“…Li et al [ 75 ] applied the concept of fuzzy measure and fuzzy integral to the classification of software defects. A complete description of the summary of software prediction models over various periods of study has been proposed by Han et al [ 76 ]. Random Forest algorithm based software prediction model developing an ensemble classifier was applied for large-scale software system [ 77 ].…”

Section: Background On Software Prediction Modelsmentioning

confidence: 99%

Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction

Kumudha¹,

Venkatesan

2016

The Scientific World Journal

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Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.

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A Summary of Software Defect Model

Cited by 2 publications

References 11 publications

Life Cycle Model of Software Defect

Life Cycle Model of Software Defect

Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction

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