Context

Bio-inspired feature selection algorithms got the attention of the researchers in the domain of Software Development Effort Estimations (SDEE) because they can improve the prediction accuracy of existing estimation techniques, such as machine learning methods.

Objective

This paper aims to analyze different feature selection algorithms and assess the role they can play to increase the accuracy of software development effort predictions.

Method

We have performed an empirical study considering commonly used bio-inspired feature selection algorithms in the domain of SDEE, i.e., Genetic Algorithm (GA), Particle Swarm Optimization, Ant Colony Optimization, Tabu Search, Harmony Search (HS), and Firefly algorithm, and four traditional non-bio-inspired algorithms, i.e., Best-First Search (BFS), Greedy Stepwise, Subset Forward Selection, and Random Search, used in combination with five widely used estimation techniques and applied to eight widely used SDEE datasets.

Results

The performed analysis suggests that almost all (bio-inspired) feature selection algorithms have outperformed the baseline estimation techniques (i.e., techniques employed without any feature selection algorithms) in the majority of the experiments and hence we can conclude that feature selection algorithms can help in the domain of SDEE to increase the prediction accuracy. Similarly, HS and GA are considered as best performed bio-inspired algorithms because they provided significantly better results than the non-bio-inspired algorithms in a greater number of experiments. Moreover, we also compared the results of various employed bio-inspired algorithms, and, again, GA and HS came out as the best performed bio-inspired feature selection algorithms.

Conclusion

From our results, if we have to pick feature selection algorithms (from both bio- and non-bio-inspired) and recommend them for future investigations, we would suggest HS because it provided better effort predictions in more combinations of datasets and estimation techniques than the other considered bio- and non-bio-inspired algorithms. Among the non-bio-inspired algorithms, BFS is the one that provided better predictions.

中文翻译：

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使用生物启发算法选择相关功能来改进软件工作量估算：一项实证研究

语境

受生物启发的特征选择算法在软件开发工作量估计（SDEE）领域引起了研究人员的关注，因为它们可以提高现有估计技术（例如机器学习方法）的预测准确性。

目的

本文旨在分析不同的特征选择算法，并评估它们可以发挥的作用，以提高软件开发工作量预测的准确性。

方法

我们已经进行了一项实验研究，考虑了SDEE领域中常用的受生物启发的特征选择算法，即遗传算法（GA），粒子群优化，蚁群优化，禁忌搜索，和声搜索（HS）和萤火虫算法，以及四种传统的非生物启发式算法，即最佳优先搜索（BFS），贪婪逐步，子集正向选择和随机搜索，与五种广泛使用的估计技术结合使用，并应用于八种广泛使用的SDEE数据集。

结果

进行的分析表明，在大多数实验中，几乎所有（受生物启发的）特征选择算法都优于基​​线估计技术（即不使用任何特征选择算法的技术），因此我们可以得出结论，特征选择算法可以帮助SDEE的范围以提高预测精度。同样，HS和GA被认为是表现最佳的生物启发算法，因为在大量实验中，它们提供的结果明显优于非生物启发算法。此外，我们还比较了各种采用的生物启发算法的结果，GA和HS再次成为表现最佳的生物启发特征选择算法。

结论

从我们的结果中，如果我们必须选择特征选择算法（来自生物和非生物启发）并推荐它们用于将来的研究，我们建议使用HS，因为它在更多的数据集和估算技术组合中提供了更好的效果预测其他被认为是生物和非生物启发算法。在非生物启发算法中，BFS是提供更好预测的算法。