Extracting software static defect models using data mining

Yousef, Ahmed H.

doi:10.1016/j.asej.2014.09.007

Cited by 24 publications

(11 citation statements)

References 56 publications

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“…On the other hand, the works [11,12] confirmed the considerable potential of Bayesian networks to explore coastal databases. The software industry has also benefited from DM techniques as seen in [13], where Ahmed H. Yousef proposed a solution architecture that enhances software development based on data in software repositories and presented a benchmark that provides an ensemble of DM models in the defective modules prediction problem and compares the results. In [14] an automatic real-time web usage data mining and recommendation system is presented.…”

Section: Related Workmentioning

confidence: 99%

Preliminary Results of an Analysis Using Association Rules to Find Relations between Medical Opinions About the non-Realization of Autopsies in a Mexican Hospital

Delgado¹,

Rodríguez-Mazahua²,

Peláez-Camarena³

et al. 2017

RCS

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In the last years, a significant reduction in the number of autopsies realized in the hospitals of the world has been observed. Since medics are the closest people to this problematic, they can offer information that helps clarify why the decreasing of this practice has occurred. In this paper, data mining techniques are applied to perform an analysis of medical opinions regarding the realization of autopsies in a hospital of Veracruz, in Mexico. The opinions were collected through surveys applied to 85 medics of the hospital. The result is a model represented by a set of rules that suggests some of the factors that are related to the decrease in the number of autopsies in the hospital, according to the survey respondents.

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Section: Related Workmentioning

confidence: 99%

Preliminary Results of an Analysis Using Association Rules to Find Relations between Medical Opinions About the non-Realization of Autopsies in a Mexican Hospital

Delgado¹,

Rodríguez-Mazahua²,

Peláez-Camarena³

et al. 2017

RCS

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“…Data mining approach was employed to show the attributes that predict the defective state of software modules and is used in large software projects to detect defective modules that will cause failures during the software execution process [ 62 ]. Meta-analysis of all relevant high quality primary studies of defect prediction was carried out to determine what factors influence predictive performance and as well to predict defect-prone software components [ 63 ].…”

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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“…There are many software defects which may cause serious problems, so in order to overcome these situations software prediction system is used. It helps to ensure the debugging and reman fastest by making it easier to improvise the program [33]. These testing and debugging helps to improvise the quality and can assure steady output from the software.…”

Section: Introductionmentioning

confidence: 99%

Software Defect Prediction using Efficient Classification Algorithms

2019

IJRTE

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The reliability of the software can be understood using the recurrence and the development of failures or it could be recognized by framework accessibility. Software can be classified as many forms such as system software, application software, shareware, literate, freeware public domain etc. Nearly, all the frameworks used to have faults and these faults results in the failure. Inorder to understand these faults, some software fault prediction is used. The main aim of these method is to predict the errors and their cause before it occurs. This article mainly discusses about the techniques which are available for the prediction of error and gives the information to understand about the data mining with NASA MDP data sets.

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Extracting software static defect models using data mining

Cited by 24 publications

References 56 publications

Preliminary Results of an Analysis Using Association Rules to Find Relations between Medical Opinions About the non-Realization of Autopsies in a Mexican Hospital

Preliminary Results of an Analysis Using Association Rules to Find Relations between Medical Opinions About the non-Realization of Autopsies in a Mexican Hospital

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

Software Defect Prediction using Efficient Classification Algorithms

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