Analysis of Search Based Algorithms for Prediction of Aging Related Bugs
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-0838-7_49
Reference27 articles.
1. Natella R (2017) Aging-related bugs and software complexity metrics. https://doi.org/10.5281/zenodo.581659
2. Sharma S, Kumar S (2018) Analysis of ensemble models for aging related bug prediction in software systems. In: Proceedings of the 13th international conference on software technologies. https://doi.org/10.5220/0006847702560263
3. Cotroneo D, Natella R, Pietrantuono R (2013) Predicting aging-related bugs using software complexity metrics. Perform Eval 70(3):163–178. https://doi.org/10.1016/j.peva.2012.09.004
4. Qin F, Zheng Z, Qiao Y, Trivedi KS (2018) Studying aging-related bug prediction using cross-project models. IEEE Trans Reliab 1–20. https://doi.org/10.1109/tr.2018.2864960
5. Khanna M, Aggarwal M, Singhal N (2021) Empirical analysis of artificial immune system algorithms for aging related bug prediction. In: 2021 7th international conference on advanced computing and communication systems (ICACCS), pp 692–697. https://doi.org/10.1109/ICACCS51430.2021.9441809
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