Context:

Nowadays, companies are investing in brand new software, given that fact they always need help with estimating software development, effort, costs, and the period of time needed for completing the software itself. In this paper, four different architectures of Artificial Neural Networks (ANN), as one of the most desired tools for predicting and estimating effort in software development, were used.

Objective:

This paper aims to determine the convergence rate of each of the proposed ANNs, when obtaining the minimum relative error, first depending on the cost effect function, then on the nature of the data on which the training, testing, and validation is performed.

Method:

Magnitude relative error (MRE) is calculated based on Taguchi’s orthogonal plans for each of these four proposed ANN architectures. The fuzzification method, five different datasets, the clustering method for input values of each dataset, and prediction were used to achieve the best model for estimation.

Results:

Based on performed parts of the experiment, it can be concluded that the convergence rate of each proposed architecture depends on the cost effect function and the nature of projects in different datasets. By following the prediction throughout all experimental parts, it can be further confirmed that ANN-L36 gave the best results in this proposed approach.

Conclusion:

The main advantages of this model are as follows: the number of iterations is less than 10, shortened effort estimation time thanks to convergence rate, simple architecture of each proposed ANN, large coverage of different values of actual project efficiency, and minimal MMRE. This model can also serve as an idea for the construction of a tool that would be able to reliably, efficiently and accurately estimate the effort when developing various software projects.

中文翻译：

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人工神经网络在软件开发项目中的估计收敛速度

语境：

如今，公司一直在购买全新的软件，因为他们总是需要帮助来估计软件开发，工作量，成本以及完成软件本身所需的时间。在本文中，使用了四种不同的人工神经网络（ANN）架构，作为预测和评估软件开发工作量的最理想工具之一。

客观的：

本文旨在确定每种拟议人工神经网络的收敛速度，当获得最小相对误差时，首先取决于成本效应函数，然后取决于进行训练，测试和验证的数据的性质。

方法：

基于田口的正交计划，针对这四种拟议的ANN体系结构，均计算了幅度相对误差（MRE）。使用模糊化方法，五个不同的数据集，每个数据集输入值的聚类方法和预测来获得最佳的估计模型。

结果：

根据实验的执行部分，可以得出结论，每种拟议架构的收敛速度取决于成本效应函数和不同数据集中项目的性质。通过遵循所有实验部分的预测，可以进一步确认ANN-L36在该拟议方法中给出了最佳结果。

结论：

该模型的主要优点如下：迭代次数少于10，由于收敛速度而缩短了工作量估计时间，每个拟议的人工神经网络的结构简单，覆盖了实际项目效率的不同值，并且最小化MMRE。该模型还可以用作构建工具的想法，该工具在开发各种软件项目时将能够可靠，有效和准确地估算工作量。