Software defect prediction using static code metrics underestimates defect-proneness

David Gray; David Bowes; N. Davey; Yi Sun; B. Christianson

doi:10.1109/IJCNN.2010.5596650

Software defect prediction using static code metrics underestimates defect-proneness

David Gray, David Bowes, N. Davey, Yi Sun, B. Christianson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

18 Citations (Scopus)

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Abstract

Many studies have been carried out to predict the presence of software code defects using static code metrics. Such studies typically report how a classifier performs with real world data, but usually no analysis of the predictions is carried out. An analysis of this kind may be worthwhile as it can illuminate the motivation behind the predictions and the severity of the misclassifications. This investigation involves a manual analysis of the predictions made by Support Vector Machine classifiers using data from the NASA Metrics Data Program repository. The findings show that the predictions are generally well motivated and that the classifiers were, on average, more 'confident' in the predictions they made which were correct.

Original language	English
Title of host publication	IEEE International Joint Conference on Neural Networks (IJCNN)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-7
Number of pages	7
ISBN (Print)	978-1-4244-6916-1
DOIs	https://doi.org/10.1109/IJCNN.2010.5596650
Publication status	Published - 2010

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Cite this

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title = "Software defect prediction using static code metrics underestimates defect-proneness",

abstract = "Many studies have been carried out to predict the presence of software code defects using static code metrics. Such studies typically report how a classifier performs with real world data, but usually no analysis of the predictions is carried out. An analysis of this kind may be worthwhile as it can illuminate the motivation behind the predictions and the severity of the misclassifications. This investigation involves a manual analysis of the predictions made by Support Vector Machine classifiers using data from the NASA Metrics Data Program repository. The findings show that the predictions are generally well motivated and that the classifiers were, on average, more 'confident' in the predictions they made which were correct.",

author = "David Gray and David Bowes and N. Davey and Yi Sun and B. Christianson",

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AU - Bowes, David

AU - Davey, N.

AU - Sun, Yi

AU - Christianson, B.

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PY - 2010

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N2 - Many studies have been carried out to predict the presence of software code defects using static code metrics. Such studies typically report how a classifier performs with real world data, but usually no analysis of the predictions is carried out. An analysis of this kind may be worthwhile as it can illuminate the motivation behind the predictions and the severity of the misclassifications. This investigation involves a manual analysis of the predictions made by Support Vector Machine classifiers using data from the NASA Metrics Data Program repository. The findings show that the predictions are generally well motivated and that the classifiers were, on average, more 'confident' in the predictions they made which were correct.

AB - Many studies have been carried out to predict the presence of software code defects using static code metrics. Such studies typically report how a classifier performs with real world data, but usually no analysis of the predictions is carried out. An analysis of this kind may be worthwhile as it can illuminate the motivation behind the predictions and the severity of the misclassifications. This investigation involves a manual analysis of the predictions made by Support Vector Machine classifiers using data from the NASA Metrics Data Program repository. The findings show that the predictions are generally well motivated and that the classifiers were, on average, more 'confident' in the predictions they made which were correct.

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BT - IEEE International Joint Conference on Neural Networks (IJCNN)

PB - Institute of Electrical and Electronics Engineers (IEEE)

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Software defect prediction using static code metrics underestimates defect-proneness

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