Inter-release defect prediction with feature selection using temporal chunk-based learning : an empirical study
Kabir, Alamgir; Keung, Jacky; Turhan, Burak; Bennin, Kwabena Ebo (2021-09-09)
Md Alamgir Kabir, Jacky Keung, Burak Turhan, Kwabena Ebo Bennin, Inter-release defect prediction with feature selection using temporal chunk-based learning: An empirical study, Applied Soft Computing, Volume 113, Part A, 2021, 107870, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2021.107870
© 2021 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
https://creativecommons.org/licenses/by-nc-nd/4.0/
https://urn.fi/URN:NBN:fi-fe2021100649449
Tiivistelmä
Abstract
Inter-release defect prediction (IRDP) is a practical scenario that employs the datasets of the previous release to build a prediction model and predicts defects for the current release within the same software project. A practical software project experiences several releases where data of each release appears in the form of chunks that arrive in temporal order. The evolving data of each release introduces new concept to the model known as concept drift, which negatively impacts the performance of IRDP models. In this study, we aim to examine and assess the impact of feature selection (FS) on the performance of IRDP models and the robustness of the model to concept drift. We conduct empirical experiments using 36 releases of 10 open-source projects. The Friedman and Nemenyi Post-hoc test results indicate that there were statistical differences between the prediction results with and without FS techniques. IRDP models trained on the data of most recent releases were not always the best models. Furthermore, the prediction models trained with carefully selected features could help reduce concept drifts.
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