During the past decades, many studies have been carried out in an attempt to build accurate software development effort estimation techniques. However, none of the techniques proposed has proven to be successful at predicting software effort in all circumstances. Among these techniques, analogy‐based estimation has gained significant popularity within software engineering community because of its outstanding performance and ability to mimic the human problem solving approach. One of the challenges facing analogy‐based effort estimation is how to predict effort when software projects are described by a mixture of continuous and categorical features. To address this issue, the present study proposes an improvement of our former 2FA‐kprototypes technique referred to as 2FA‐cmeans. 2FA‐cmeans uses a clustering technique, called general fuzzy c‐means, which is a generalization of the fuzzy c‐means clustering technique to cluster objects with mixed attributes. The performance of 2FA‐cmeans was evaluated and compared with that of our former 2FA‐kprototypes technique as well as classical analogy over six datasets that are quite diverse and have different sizes. Empirical results showed that 2FA‐cmeans outperforms the two other analogy techniques using both all‐in and jackknife evaluation methods. This was also confirmed by the win–tie–loss statistics and the Scott–Knott test.

中文翻译：

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使用基于模糊聚类的类比估算软件开发工作量

在过去的几十年中，进行了许多研究，以尝试建立准确的软件开发工作量估算技术。但是，在所有情况下，所提出的技术均无法成功预测软件的工作量。在这些技术中，基于类比的估计由于其出色的性能和模仿人类问题解决方法的能力而在软件工程界引起了极大的欢迎。基于类比​​的工作量估算面临的挑战之一是，当软件项目由连续和分类特征的混合描述时，如何预测工作量。为了解决这个问题，本研究提出了对我们以前称为2FA-cmeans的2FA-k原型技术的改进。2FA-cmeans使用一种称为通用模糊c-均值的聚类技术，这是模糊c均值聚类技术的概括，用于聚类具有混合属性的对象。对2FA-cmeans的性能进行了评估，并与我们以前的2FA-k原型技术以及经典的类比技术比较了六个非常不同且大小不同的数据集。实证结果表明，使用全进和折刀评估方法，2FA均优于其他两种类比技术。胜负关系统计数据和Scott-Knott检验也证实了这一点。实证结果表明，使用全进和折刀评估方法，2FA均优于其他两种类比技术。胜负关系统计数据和Scott-Knott检验也证实了这一点。实证结果表明，使用全进和折刀评估方法，2FA均优于其他两种类比技术。胜负关系统计数据和Scott-Knott检验也证实了这一点。