Analogy-Based Estimation (ABE) is a popular method for non-algorithmic estimation due to its simplicity and effectiveness. The Analogy-Based Estimation (ABE) model was proposed by researchers, however, no optimal approach for reliable estimation was developed. Achieving high accuracy in the ABE might be challenging for new software projects that differ from previous initiatives. This study (conducted in June 2024) proposes a Firefly Algorithm-guided Analogy-Based Estimation (FAABE) model that combines FA with ABE to improve estimation accuracy. The FAABE model was tested on five publicly accessible datasets: Cocomo81, Desharnais, China, Albrecht, Kemerer and Maxwell. To improve prediction efficiency, feature selection was used. The results were measured using a variety of evaluation metrics; various error measures include MMRE, MAE, MSE, and RMSE. Compared to conventional models, the experimental results show notable increases in prediction precision, demonstrating the efficacy of the Firefly-Analogy ensemble.
翻译：类比估算（ABE）因其简单性和有效性而成为非算法估算的常用方法。尽管研究人员提出了类比估算（ABE）模型，但尚未开发出可靠估算的最优方法。对于与以往项目不同的新软件项目，在ABE中实现高精度可能具有挑战性。本研究（于2024年6月进行）提出了一种萤火虫算法引导的类比估算（FAABE）模型，该模型将FA与ABE相结合以提高估算精度。FAABE模型在五个公开可访问的数据集上进行了测试：Cocomo81、Desharnais、China、Albrecht、Kemerer和Maxwell。为提高预测效率，采用了特征选择技术。结果通过多种评估指标进行衡量；各类误差度量包括MMRE、MAE、MSE和RMSE。与传统模型相比，实验结果显示预测精度显著提升，证明了萤火虫-类比集成方法的有效性。
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