Size-biased Hybrid Model for Software Defect Prediction
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12597-024-00832-7.pdf
Reference24 articles.
1. Chakraborty, A.K., Gijo, E.V., Das, A., Chatterjee, M.: Hardware and software reliability, verification, and testing. In: Pham, H. (ed) Springer Handbook of Engineering Statistics. Springer Handbooks, pp. 415–442. Springer, London (2023). https://doi.org/10.1007/978-1-4471-7503-2_22
2. Dewanji, A., Sengupta, D, Chakraborty, A.K.: Discrete time model for software reliability with application to a flight control software. Appl. Stoch. Models Bus. Ind. 27(6), 723–731 (2011). https://doi.org/10.1002/asmb.902A
3. Chakraborty, T., Chakraborty, A.K.: Hellinger net: a hybrid imbalance learning model to improve software defect prediction. IEEE Trans. Reliab. 70(2), 481–494 (2021). https://doi.org/10.1109/TR.2020.3020238
4. Chakraborty, A.K., Karmakar, B.: Software defect prediction through a hybrid approach comprising of a statistical tool and a machine learning model. In: Gunasekaran, A., Sharma, J.K., Kar, S. (eds.) Applications of Operational Research in Business and Industries. Lecture Notes in Operations Research, Springer, pp. 1–19. Singapore (2023). https://doi.org/10.1007/978-981-19-8012-11
5. D’Amico, Guglielmo: Single-use reliability computation of a semi-Markovian system. Appl. Math. 59, 571–588 (2014)
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