用萤火虫算法优化软件工作量预测，提升准确性。
Enhancing Analogy-Based Software Effort Estimation with Firefly Algorithm Optimization
- 引入萤火虫算法优化相似案例匹配，改进传统方法。
- 在5个数据集上测试，误差指标均优于传统模型。
- 适合需要高精度估算的新软件项目参考。
基于案例的估算（ABE）因简单有效而广受欢迎，但缺乏可靠的最优估算方法。对于与以往项目差异较大的新项目，实现高精度估算仍具挑战。本研究（2024年6月完成）提出一种萤火虫算法引导的基于案例估算（FAABE）模型，将萤火虫算法与ABE结合以提升预测精度。该模型在五个公开数据集（Cocomo81、Desharnais、China、Albrecht、Kemerer、Maxwell）上进行测试，并采用特征选择提升预测效率。通过多种评估指标（包括MMRE、MAE、MSE、RMSE）衡量结果。实验表明，相较于传统模型，FAABE在预测精度上显著提升，验证了萤火虫-案例集成方法的有效性。
原文摘要 · Abstract (English)
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.
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