@inproceedings{f14325ca3041459e9b34bbbf3d59986d,
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",
note = "“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.{"} “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”",
year = "2010",
doi = "10.1109/IJCNN.2010.5596650",
language = "English",
isbn = "978-1-4244-6916-1",
pages = "1--7",
booktitle = "IEEE International Joint Conference on Neural Networks (IJCNN)",
publisher = "IEEE",
}