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A Transfer Weighted Homogeneous Ensemble Method for Cross-Project Software Fault Prediction.

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Název: A Transfer Weighted Homogeneous Ensemble Method for Cross-Project Software Fault Prediction.
Autoři: Khatri, Yogita1 (AUTHOR) yogitabaveja65@gmail.com, Saxena, Urvashi Rahul1 (AUTHOR) urvashirahulsaxena@gmail.com
Zdroj: International Journal of Reliability, Quality & Safety Engineering. Oct2025, Vol. 32 Issue 5, p1-28. 28p.
Témata: *COMPUTER software, ENSEMBLE learning, MACHINE tools, HETEROGENEITY, VOTING
Abstrakt: The selection of learners plays a vital role in constructing a robust and trustworthy software fault prediction (SFP) model. Every learner is distinct in terms of its predictive power. Therefore, choosing the ideal learner becomes difficult, especially for cross-project software fault prediction (CPSFP), which relies on external data (aka reference data) for model building. Although ensemble methods have shown promising results, the recent state-of-the-art is still under-explored. A majority of the proposed ensemble techniques merge the outcome of several diverse base classifiers. Unlike them, to further improve the predictive power, we propose a Transfer Weighted Homogeneous Ensemble Learner (TWHEL), which first assigns the transfer weights to reference data samples and then combines the output of various sub-learners trained by applying the same machine learning algorithm on different folds of the weighted reference data generated using cross-validation. To examine the potency of TWHEL, it is compared with two radical methods: Validation and Voting and ASCI on 21 datasets. We achieve an average improvement of 15.08% to 47.51% and 59.60% to 170.8% w.r.t. MCC and F-measure, respectively, over the compared methods. Further, the results of statistical testing corroborate the findings. We thus conclude that practitioners can create reliable, superior-quality software at a reduced cost using the presented approach. [ABSTRACT FROM AUTHOR]
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Abstrakt:The selection of learners plays a vital role in constructing a robust and trustworthy software fault prediction (SFP) model. Every learner is distinct in terms of its predictive power. Therefore, choosing the ideal learner becomes difficult, especially for cross-project software fault prediction (CPSFP), which relies on external data (aka reference data) for model building. Although ensemble methods have shown promising results, the recent state-of-the-art is still under-explored. A majority of the proposed ensemble techniques merge the outcome of several diverse base classifiers. Unlike them, to further improve the predictive power, we propose a Transfer Weighted Homogeneous Ensemble Learner (TWHEL), which first assigns the transfer weights to reference data samples and then combines the output of various sub-learners trained by applying the same machine learning algorithm on different folds of the weighted reference data generated using cross-validation. To examine the potency of TWHEL, it is compared with two radical methods: Validation and Voting and ASCI on 21 datasets. We achieve an average improvement of 15.08% to 47.51% and 59.60% to 170.8% w.r.t. MCC and F-measure, respectively, over the compared methods. Further, the results of statistical testing corroborate the findings. We thus conclude that practitioners can create reliable, superior-quality software at a reduced cost using the presented approach. [ABSTRACT FROM AUTHOR]
ISSN:02185393
DOI:10.1142/S0218539325500093