Applying fuzzy neural network to estimate software development effort

The ability to accurately and consistently estimate software development efforts is required by the project managers in planning and conducting software development activities. Since software effort drivers are vague and uncertain, software effort estimates, especially in the early stages of the development life cycle, are prone to a certain degree of estimation errors. A software effort estimation model which adopts a fuzzy inference method provides a solution to fit the uncertain and vague properties of software effort drivers. The present paper proposes a fuzzy neural network (FNN) approach for embedding artificial neural network into fuzzy inference processes in order to derive the software effort estimates. Artificial neural network is utilized to determine the significant fuzzy rules in fuzzy inference processes. We demonstrated our approach by using the 63 historical project data in the well-known COCOMO model. Empirical results showed that applying FNN for software effort estimates resulted in slightly smaller mean magnitude of relative error (MMRE) and probability of a project having a relative error of less than or equal to 0.25 (Pred(0.25)) as compared with the results obtained by just using artificial neural network and the original model. The proposed model can also provide objective fuzzy effort estimation rule sets by adopting the learning mechanism of the artificial neural network.     

Software effort estimation based on the optimal Bayesian belief network

In this paper, we present a model for software effort (person-month) estimation based on three levels Bayesian network and 15 components of COCOMO and software size. The Bayesian network works with discrete intervals for nodes. However, we consider the intervals of all nodes of network as fuzzy numbers. Also, we obtain the optimal updating coefficient of effort estimation based on the concept of optimal control using Genetic algorithm and Particle swarm optimization for the COCOMO NASA database. In the other words, estimated value of effort is modified by determining the optimal coefficient. Also, we estimate the software effort with considering software quality in terms of the number of defects which is detected and removed in three steps of requirements specification, design and coding. If the number of defects is more than the specified threshold then the model is returned to the current step and an additional effort is added to the estimated effort. The results of model indicate that optimal updating coefficient obtained by genetic algorithm increases the accuracy of estimation significantly. Also, results of comparing the proposed model with the other ones indicate that the accuracy of the model is more than the other models.     

An investigation of artificial neural networks based prediction systems in software project management

A critical issue in software project management is the accurate estimation of size, effort, resources, cost, and time spent in the development process. Underestimates may lead to time pressures that may compromise full functional development and the software testing process. Likewise, overestimates can result in noncompetitive budgets. In this paper, artificial neural network and stepwise regression based predictive models are investigated, aiming at offering alternative methods for those who do not believe in estimation models. The results presented in this paper compare the performance of both methods and indicate that these techniques are competitive with the APF, SLIM, and COCOMO methods.     

Improving the COCOMO model using a neuro-fuzzy approach

Accurate software development cost estimation is important for effective project management such as budgeting, project planning and control. So far, no model has proved to be successful at effectively and consistently predicting software development cost. A novel neuro-fuzzy Constructive Cost Model (COCOMO) is proposed for software cost estimation. This model carries some of the desirable features of a neuro-fuzzy approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural network approach, the proposed model can be interpreted and validated by experts, and has good generalization capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. In addition, it allows input to have continuous rating values and linguistic values, thus avoiding the problem of similar projects having large different estimated costs. A detailed learning algorithm is also presented in this work. The validation using industry project data shows that the model greatly improves estimation accuracy in comparison with the well-known COCOMO model.     

基于RBF神经网络的软件成本估算模型

软件成本估算是软件开发过程中一项非常重要的活动,但现有的方法在准确估算软件成本方面还存在不足。针对软件成本估算不够准确的现状,提出了一种基于RBF神经网络的软件成本估算模型。该模型采用样本聚类的方法确定隐含层节点数,利用遗传算法对隐层节点中心值和高斯函数的宽度进行优化,利用线性最小二乘法训练网络的权值。实验证明,该模型能够准确有效地估算软件成本。     

An empirical validation of a neural network model for software effort estimation

As software becomes more complex and its scope dramatically increases, the importance of research on developing methods for estimating software development efforts has perpetually increased. Such accurate estimation has a prominent impact on the success of projects. Out of the numerous methods for estimating software development efforts that have been proposed, line of code (LOC)-based constructive cost model (COCOMO), function point-based regression model (FP), neural network model (NN), and case-based reasoning (CBR) are among the most popular models. Recent research has tended to focus on the use of function points (FPs) in estimating the software development efforts, however, a precise estimation should not only consider the FPs, which represent the size of the software, but should also include various elements of the development environment for its estimation. Therefore, this study is designed to analyze the FPs and the development environments of recent software development cases. The primary purpose of this study is to propose a precise method of estimation that takes into account and places emphasis on the various software development elements. This research proposes and evaluates a neural network-based software development estimation model.     

改进的BP算法在建筑工程造价估算中的应用

建筑工程造价估算是项目可行性研究阶段的重要内容.研究了工程造价及其影响因素之间复杂的非线性关系,改进了人工神经网络BP算法,在此基础上建立建筑工程造价估算模型.为了验证模型的正确性,收集了莱芜市20个典型建筑工程项目,并选取其中18个作为训练样本,2个作为测试样本,运用MATLAB建模分析,测试结果表明,预测精度与实际值偏差不大,精度满足要求.神经网络在工程造价估算方面具有良好的发展前景.     

Software development cost estimation using wavelet neural networks

Software development has become an essential investment for many organizations. Software engineering practitioners have become more and more concerned about accurately predicting the cost and quality of software product under development. Accurate estimates are desired but no model has proved to be successful at effectively and consistently predicting software development cost. In this paper, we propose the use of wavelet neural network (WNN) to forecast the software development effort. We used two types of WNN with Morlet function and Gaussian function as transfer function and also proposed threshold acceptance training algorithm for wavelet neural network (TAWNN). The effectiveness of the WNN variants is compared with other techniques such as multilayer perceptron (MLP), radial basis function network (RBFN), multiple linear regression (MLR), dynamic evolving neuro-fuzzy inference system (DENFIS) and support vector machine (SVM) in terms of the error measure which is mean magnitude relative error (MMRE) obtained on Canadian financial (CF) dataset and IBM data processing services (IBMDPS) dataset. Based on the experiments conducted, it is observed that the WNN-Morlet for CF dataset and WNN-Gaussian for IBMDPS outperformed all the other techniques. Also, TAWNN outperformed all other techniques except WNN.     

测算在软件开发中的实践①

软件测算是软件开发工程化管理的重要技术。但软件本身的复杂性、历史经验的缺乏、估算工具不完备性以及人为错误,导致软件项目的估算结果往往和实际情况相差甚远。为了改进测算结果,根据软件生命周期理论,结合COCOMOⅡ模型,指出了软件开发模型中的关键测算点及相应可采用的测算方法,并且明确了软件测算人员应具备的素质要求及在典型的软件组织中的地位。最后通过实例,使用USC提供的一个免费软件测算工具对一个具体的软件项目进行测算,较好地控制开发过程。     

项目度量中对基本COCOMO进行校准的研究

所有成功的软件组织都将度量作为保证自己管理和技术质量的重要手段,软件成本估计则是软件度量[1,2]的核心任务。为了提高成本估算的准确性,文中根据特定软件企业中的历史项目数据对基本COCOMO模型进行校准,在具体的参数修正方法上利用对数数据相关算法进行校正,并与其它方法进行了比较,得到了满意的结果。校准后的模型对项目开发成本的预测将会更加准确,从而切实体现COCOMO成本度量工作对于软件项目的指导价值。因此,文中所做的成本估算模型的校准工作,对软件开发企业非常具有实用价值。     

RS-GA神经网络无人机受风情况估计

针对现有无人机（Unmanned Aerial Vehicle,UAV）风场估计方法中存在的计算复杂、需额外搭载传感器等问题,提出基于粗糙集遗传神经网络的无人机受风状态估计方法。该方法利用粗糙集分析方法对无人机上采集的姿态信息数据集进行约简;利用遗传算法全局搜索能力强的特点优化神经网络的初始权值;用简化的无人机数据集训练神经网络即得到所需神经网络风场估计模型。仿真结果表明,该方法具有较高的识别率以及较短的训练时间,证明了其在无人机风场估计上应用的有效性。     

Cost models for future software life cycle processes: COCOMO 2.0

Current software cost estimation models, such as the 1981 Constructive Cost Model (COCOMO) for software cost estimation and its 1987 Ada COCOMO update, have been experiencing increasing difficulties in estimating the costs of software developed to new life cycle processes and capabilities. These include non-sequential and rapid-development process models; reuse-driven approaches involving commercial off-the-shelf (COTS) packages, re-engineering, applications composition, and applications generation capabilities; object-oriented approaches supported by distributed middleware; and software process maturity initiatives. This paper summarizes research in deriving a baseline COCOMO 2.0 model tailored to these new forms of software development, including rationale for the model decisions. The major new modeling capabilities of COCOMO 2.0 are a tailorable family of software sizing models, involving Object Points, Function Points, and Source Lines of Code; nonlinear models for software reuse and re-engineering; an exponentdriver approach for modeling relative software diseconomies of scale; and several additions, deletions and updates to previous COCOMO effort-multiplier cost drivers. This model is serving as a framework for an extensive current data collection and analysis effort to further refine and calibrate the model's estimation capabilities.     

利用自监督卷积网络估计单图像深度信息

为了提高利用深度神经网络预测单图像深度信息的精确度,提出了一种采用自监督卷积神经网络进行单图像深度估计的方法.首先,该方法通过在编解码结构中引入残差结构、密集连接结构和跳跃连接等方式改进了单图像深度估计卷积神经网络,改善了网络的学习效率和性能,加快了网络的收敛速度;其次,通过结合灰度相似性、视差平滑和左右视差匹配等损失度量设计了一种更有效的损失函数,有效地降低了图像光照因素影响,遏制了图像深度的不连续性,并能保证左右视差的一致性,从而提高深度估计的鲁棒性;最后,采用立体图像作为训练数据,无需目标深度监督信息,实现了端到端的单幅图像深度估计.在TensorFlow框架下,用KITTI和Cityscapes数据集进行实验,结果表明,与目前的主流方法相比,该方法在预测深度的精确度方面有较大提升,拥有更好的深度预测性能.     

Knowledge-based risk assessment and cost estimation

A knowledge-based method for software project risk assessment and cost estimation has been implemented on multiple platforms. As an extension to the Constructive Cost Model (COCOMO), it aids in project planning by identifying, categorizing, quantifying and prioritizing project risks. It also detects cost estimate input anomalies and provides risk control advice in addition to conventional COCOMO cost and schedule calculation.The method has been developed in conjunction with a system dynamics model of the software development process, and serves as an intelligent front end to the simulation model. It extends previous research in the knowledge-based cost estimation domain by focusing on risk assessment, incorporating substantially more rules, going beyond standard COCOMO, performing quantitative validation, providing a user-friendly interface, and integrating it with a dynamic simulation model.Results of the validation are promising, and the method is being used at Litton Data Systems and other industrial environments. It will be undergoing further enhancement as part of an integrated capability for software engineering to assist in system acquisition, project planning and risk management.     

软件开发成本估算模型的研究

软件成本准确地估算和管理控制是软件项目开发成功的基础。该文分析了影响软件开发成本估算准确性的因素,并详细介绍了IBM模型、Putnam模型和COCOMO模型三种经验估算模型,探讨了软件开发成本估算的新需求。     

An empirical study using task assignment patterns to improve theaccuracy of software effort estimation

In most software development organizations, there is seldom a one-to-one mapping between software developers and development tasks. It is frequently necessary to concurrently assign individuals to multiple tasks and to assign more than one individual to work cooperatively on a single task. A principal goal in making such assignments should be to minimize the effort required to complete each task. But what impact does the manner in which developers are assigned to tasks have on the effort requirements? This paper identifies four task assignment factors: team size, concurrency, intensity, and fragmentation. These four factors are shown to improve the predictive ability of the well-known intermediate COCOMO cost estimation model. A parsimonious effort estimation model is also derived that utilizes a subset of the task assignment factors and unadjusted function points. For the data examined, this parsimonious model is shown to have goodness of fit and quality of estimation superior to that of the COCOMO model, while utilizing fewer cost factors     

构建基于软件过程的成本模型

在分析COCOMOⅡ模型的基础上,提出了一个基于软件过程的成本模型,其中的数量模型既可以避免COCOMO模型度量的复杂性,又可以根据实际数据描述非线性的成本与驱动因素之间的映射关系.该模型还描述了在实际软件过程中成本度量方法,以及利用过程成本度量数据实现估算和成本控制的机制.     

一种无线传感器网络缺失数值估计算法

无线传感器网络（WSNs）工作环境复杂,不可避免会出现感知数值缺失问题。提出一种基于BP神经网络模型的缺失数值估计算法,利用同节点多参数间相关性特点,以强相关参数集为输入进行缺失数值输出估计。为了提高数值估计准确性和稳定性,提出将上述算法与线性回归算法结合,对二种缺失数值估计量进行加权平均,针对变化规律复杂的缺失数值进行有效的估计。基于实际采样数值进行仿真分析,结果表明：算法能够有效地完成缺失数值估计,同时对WSNs拓扑结构和节点覆盖率依赖性较弱,实用性较好。     

基于人工神经网络的软件失效预测模型研究

在软件开发的早期预测有失效倾向的软件模块,能够极大的提高软件的质量。软件失效预测中的一个普遍的问题是数据中存在噪声,而神经网络具有鲁棒性并对噪声有很强的抑制能力。该文介绍了一种基于人工神经网络的软件失效预测模型,给出了基于反向传播算法的多层前向网络的网络结构。用这种方法对朗讯光网络有限公司开发的SDH通信软件进行了分析,并得到了较高的预测准确率。通过采集通信软件的不同发布版本的测试历史数据,讨论了训练集数据的选择与预测精度之间的关系。     

An Empirical Study of Analogy-based Software Effort Estimation

Conventional approaches to software cost estimation have focused on algorithmic cost models, where an estimate of effort is calculated from one or more numerical inputs via a mathematical model. Analogy-based estimation has recently emerged as a promising approach, with comparable accuracy to algorithmic methods in some studies, and it is potentially easier to understand and apply. The current study compares several methods of analogy-based software effort estimation with each other and also with a simple linear regression model. The results show that people are better than tools at selecting analogues for the data set used in this study. Estimates based on their selections, with a linear size adjustment to the analogue's effort value, proved more accurate than estimates based on analogues selected by tools, and also more accurate than estimates based on the simple regression model.