Validating Unsupervised Machine Learning Techniques for Software Defect Prediction with Generic Metamorphic Testing

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

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Computer Science

100
Testing Computer Science
100
Machine Learning Te... Computer Science
100
Software Defect Computer Science
100
Defect Prediction Computer Science
80
Prediction Model Computer Science
60
Model Computer Science
10
Algorithms Computer Science
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Application Computer Science
10
Mutation Operator Computer Science
10
Training Dataset Computer Science
10
Bypassing Computer Science
10
Machine Learning Al... Computer Science
10
Agglomerative Clust... Computer Science
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Dataset Source Computer Science
10
Procedure Computer Science

Keyphrases

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Metamorphic Testing Keyphrases
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Software Defect Pre... Keyphrases
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Unsupervised Machin... Keyphrases
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Metamorphic Relatio... Keyphrases
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Cluster-based Keyphrases
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Inconsistency Keyphrases
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Nineteen Keyphrases
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Mutation Operator Keyphrases
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Public Dataset Keyphrases
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Labeled Data Keyphrases
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Generic Data Keyphrases
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Defect Prediction Keyphrases
11
Machine Learning Al... Keyphrases
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Density-based Spati... Keyphrases
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Model Sensitivity Keyphrases
11
Unsupervised Model Keyphrases
11
Data mutation Keyphrases
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Without Prior Knowl... Keyphrases
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Model-independent Keyphrases
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Agglomerative Clust... Keyphrases
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Dataset Training Keyphrases

INIS

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defects INIS
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testing INIS
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prediction INIS
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computer codes INIS
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machine learning INIS
36
datasets INIS
18
algorithms INIS
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expansion INIS
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data INIS
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validation INIS
18
shrinkage INIS
9
applications INIS
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density INIS
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sensitivity INIS
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noise INIS
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mutations INIS
Research OutputsResearch Output authored by Validating Unsupervised Machine Learning Techniques for Software Defect Prediction with Generic Metamorphic Testing is tagged with the concept