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- W4288077334 abstract "Context: Reliability prediction is critical for software engineers in the current challenging scenario of increased demand for high-quality software. Even though various software reliability prediction models have been established so far, there is always a need for a more accurate model in today's competitive environment for producing high-quality software. Objective: This paper proposes a neuro-fuzzy hybridized method by integrating self-organized- map (SOM) and fuzzy time series (FTS) forecasting for the reliability prediction of a software system. Methodology: In the proposed approach, a well-known supervised clustering algorithm SOM is incorporated with FTS forecasting for developing a hybrid model for software reliability prediction. To validate the proposed approach, an experimental study is done by applying proposed neuro-fuzzy method on a software failure dataset. In addition, a comparative study was conducted for evaluating the performance of the proposed method by comparing it with some of the existing FTS models. Results: Experimental outcomes show that the proposed approach performs better than the existing FTS models. Conclusion: The results show that the proposed approach can be used efficiently in the software industry for software reliability prediction." @default.
- W4288077334 created "2022-07-28" @default.
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- W4288077334 date "2022-07-28" @default.
- W4288077334 modified "2023-09-30" @default.
- W4288077334 title "A Neuro-Fuzzy Hybridized Approach for Software Reliability Prediction" @default.
- W4288077334 doi "https://doi.org/10.3897/jucs.80537" @default.
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