A practical method for the software fault-prediction

by: Zhan Li, M Reformat

Information Reuse and Integration, 2007. IRI 2007. IEEE International Conference on (2007), pp. 659-666.

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In the paper, a novel machine learning method, SimBoost, is proposed to handle the software fault-prediction problem when highly skewed datasets are used. Although the method, proved by empirical results, can make the datasets much more balanced, the accuracy of the prediction is still not satisfactory. Therefore, a fuzzy-based representation of the software module fault state has been presented instead of the original faulty/non-faulty one. Several experiments were conducted using datasets from NASA Metrics Data Program. The discussion of the results of experiments is provided.

@inproceedings{citeulike:2858306, abstract = {In the paper, a novel machine learning method, SimBoost, is proposed to handle the software fault-prediction problem when highly skewed datasets are used. Although the method, proved by empirical results, can make the datasets much more balanced, the accuracy of the prediction is still not satisfactory. Therefore, a fuzzy-based representation of the software module fault state has been presented instead of the original faulty/non-faulty one. Several experiments were conducted using datasets from NASA Metrics Data Program. The discussion of the results of experiments is provided.}, author = {Li, Zhan and Reformat, M. }, booktitle = {Information Reuse and Integration, 2007. IRI 2007. IEEE International Conference on}, citeulike-article-id = {2858306}, doi = {http://dx.doi.org/10.1109/IRI.2007.4296695}, journal = {Information Reuse and Integration, 2007. IRI 2007. IEEE International Conference on}, keywords = {boosting, imbalanced}, pages = {659--666}, posted-at = {2008-06-03 06:42:12}, priority = {2}, title = {A practical method for the software fault-prediction}, url = {http://dx.doi.org/10.1109/IRI.2007.4296695}, year = {2007} }

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TY - CONF ID - citeulike:2858306 L3 - citeulike-article-id:2858306 TI - A practical method for the software fault-prediction T2 - Information Reuse and Integration, 2007. IRI 2007. IEEE International Conference on JF - Information Reuse and Integration, 2007. IRI 2007. IEEE International Conference on SP - 659 EP - 666 N2 - In the paper, a novel machine learning method, SimBoost, is proposed to handle the software fault-prediction problem when highly skewed datasets are used. Although the method, proved by empirical results, can make the datasets much more balanced, the accuracy of the prediction is still not satisfactory. Therefore, a fuzzy-based representation of the software module fault state has been presented instead of the original faulty/non-faulty one. Several experiments were conducted using datasets from NASA Metrics Data Program. The discussion of the results of experiments is provided. KW - boosting KW - imbalanced AU - Li, Zhan AU - Reformat, M PY - 2007/// UR - http://dx.doi.org/10.1109/IRI.2007.4296695 ER -

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