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Predicting software faults in large space systems using machine learning techniques

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dc.contributor.author Twala, Bhekisipho
dc.date.accessioned 2012-09-19T06:09:24Z
dc.date.available 2012-09-19T06:09:24Z
dc.date.issued 2011-07-04
dc.identifier.citation Twala, B. 2011. Predicting software faults in large space systems using machine learning techniques. Defence Science Journal, 61(4):306-316. en_US
dc.identifier.issn 0011-748X
dc.identifier.uri http://hdl.handle.net/10210/7754
dc.description.abstract Recently, the use of machine learning (ML) algorithms has proven to be of great practical value in solving a variety of engineering problems including the prediction of failure, fault, and defect-proneness as the space system software becomes complex. One of the most active areas of recent research in ML has been the use of ensemble classifiers. How ML techniques (or classifiers) could be used to predict software faults in space systems, including many aerospace systems is shown, and further use ensemble individual classifiers by having them vote for the most popular class to improve system software fault-proneness prediction. Benchmarking results on four NASA public datasets show the Naive Bayes classifier as more robust software fault prediction while most ensembles with a decision tree classifier as one of its components achieve higher accuracy rates. en_US
dc.language.iso en en_US
dc.publisher Defence Science Journal en_US
dc.rights DESIDOC en_US
dc.subject Software metrics en_US
dc.subject Machine learning en_US
dc.subject Fault-proneness prediction en_US
dc.subject Ensemble classifiers en_US
dc.title Predicting software faults in large space systems using machine learning techniques en_US
dc.type Article en_US


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