@inproceedings{da07043139df4d589771d4e36b2cc682,
title = "Performance tuning for automotive Software Fault Prediction",
abstract = "Fault prediction on high quality industry grade software often suffers from strong imbalanced class distribution due to a low bug rate. Previous work reports on low predictive performance, thus tuning parameters is required. As the State of the Art recommends sampling methods for imbalanced learning, we analyse effects when under- and oversampling the training data evaluated on seven different classification algorithms. Our results demonstrate settings to achieve higher performance values but the various classifiers are influenced in different ways. Furthermore, not all performance reports can be tuned at the same time.",
author = "Harald Altinger and Steffen Herbold and Friederike Schneemann and Jens Grabowski and Franz Wotawa",
year = "2017",
month = mar,
day = "21",
doi = "10.1109/SANER.2017.7884667",
language = "English",
series = "SANER 2017 - 24th IEEE International Conference on Software Analysis, Evolution, and Reengineering",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "526--530",
editor = "Gabriele Bavota and Martin Pinzger and Andrian Marcus",
booktitle = "SANER 2017 - 24th IEEE International Conference on Software Analysis, Evolution, and Reengineering",
address = "United States",
note = "24th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2017 ; Conference date: 21-02-2017 Through 24-02-2017",
}