一种面向ERP外包项目的开发成本估算方法

对于软件项目而言,项目成本的有效控制是每个项目取得成功的标志之一。恰当的软件开发成本估算方法将大大提高成本估算的稳定性和可靠性,从而提高项目经理对项目成本的有效控制。本文在深入分析目前业界常用的软件项目开发成本估算方法的基础上,针对ERP外包软件项目开发生命周期的特点,提出了以ERP程序单元为最小单位的一种项目开发成本估算法,即FRICE估算法。该估算方法已经在大量ERP外包软件项目中得到了成功应用、实践和验证,它能有效地帮助项目经理对项目开发成本进行估算、控制和管理。     

The increase of productivity over time—an industrial case study

Introducing new and specialized technology is often seen as a way of meeting increasing non-functional requirements. An example of such a technology is a software platform that provides high performance and availability. The novelty of such a platform and lack of related experience and competence among the staff may affect initial development productivity. The competence problems should disappear with time. In this paper, we present a study, which we conducted at Ericsson. The purpose of the study was to assess the impact of experience and maturity on productivity in software development on the specialized platform. We quantify the impact by comparing productivity of two projects. One represents an initial development stage while the other represents a subsequent and thus more matured development stage. Both projects resulted in large commercial products. We reveal a factor of four difference in productivity. The difference was caused by a higher code delivery rate and a lower number of code lines per functionality in the latter project. We assess the impact of both these issues on productivity and explain their nature. Based on our findings, we suggest a number of improvement suggestions and guidelines for the process of introducing a new technology.     

基于水利信息化系统软件成本测算方法的分析

水利信息化建设近年来得到空前的重视和强大的推力,但如何度量和评估软件项目成本仍是水利行业软系统开发项目预算管理和决算审计过程中亟待解决的问题,本文阐述了软件开发生命周期、软件成本构成及软件测算方法,重点结合某市防汛会商平台系统的开发,采用功能点法对软件开发费用进行合理的测算,运用相关知识进行功能点规模的估算,为今后水利同类软系统项目的概预算管理工作提供有力技术支撑,推动水利软件成本测算的合理性和科学化。     

Range estimation of construction costs using neural networks with bootstrap prediction intervals

Modeling of construction costs is a challenging task, as it requires representation of complex relations between factors and project costs with sparse and noisy data. In this paper, neural networks with bootstrap prediction intervals are presented for range estimation of construction costs. In the integrated approach, neural networks are used for modeling the mapping function between the factors and costs, and bootstrap method is used to quantify the level of variability included in the estimated costs. The integrated method is applied to range estimation of building projects. Two techniques; elimination of the input variables, and Bayesian regularization were implemented to improve generalization capabilities of the neural network models. The proposed modeling approach enables identification of parsimonious mapping function between the factors and cost and, provides a tool to quantify the prediction variability of the neural network models. Hence, the integrated approach presents a robust and pragmatic alternative for conceptual estimation of costs.     

水利信息化项目设计概(估)算编制研究

为规范水利信息化项目设计概（估）算编制,合理确定投资,针对水利信息化项目多学科、多行业交叉融合的特点,通过分析国内外相关标准规定,调研典型水利信息化项目实际情况,侧重解决概（估）算编制的重难点问题。提出符合水利信息化实际的项目划分、费用组成与单价构成,并结合软、硬件费用编制的特点,给出相关计算方法,最后通过实例应用验证相关结论。结果表明：水利信息化项目有别于常规水利工程以土木工程为主的特点,是以附加值含量高的软硬件设施设备为主体的新型基础设施项目,设计概（估）算编制应注重合理确定软硬件设备价格,根据项目实际情况科学划分并计算相关费用,才能满足项目设计概（估）算阶段投资控制的需要。     

Software development cost estimation: Integrating neural network with cluster analysis

For software project planning control and management, an accurate estimate of software development cost is important. Past research has focused on using parametric models to predict development cost based on attributes such as lines of code or function points. This requires researchers to identify the set of factors that influence cost estimation before the system is constructed. We propose a non-parametric approach that integrates a neural network method with cluster analysis to estimate development cost. The integration of the two techniques not only allows for a more accurate cost estimate but also leads to an increase in the training efficacy of the network.     

A Study of Reasoning Processes in Software Maintenance Management

More resources are spent on maintaining software than for its development. Maintenance costs for large scale software systems can amount to somewhere between 40 and 67% of the total system life cycle cost. It is therefore important to manage maintenance costs, and to balance costs with benefits. Frequently this task is approached, at least in the literature, merely as a software cost estimation problem. Unfortunately, the creation of effort estimation models for maintenance – a primary requisite for cost calculation – has not yet been satisfactorily addressed. At the same time, project managers do not estimate costs first, but instead prioritize maintenance projects, trying to determine which projects to carry out (first) within their fixed budgets and resource capabilities. This essentially means that cost estimation is done qualitatively first before formal cost estimation techniques are employed. Recognizing the problems associated with standard, regression based estimation models, and focusing on the needs of software project managers, this research studied the process of project prioritization as an expert problem solving and decision making task, through concurrently taken (think aloud) protocols. Analysis of these protocols revealed that experts rarely make use of formal mathematical models to determine project priorities or resource needs, such as COCOMO or FPA, although project size is a key determinant of a project's priority. Instead, estimators qualitatively consider cost or value, urgency, and difficulty of a maintenance task, then prioritize projects accordingly, followed by a decision concerning further treatment of the problem. The process employs case based reasoning and the use of heuristics. While different experts may use different strategies, there exists great overlap in their overall prioritization procedure.     

MND-SCEMP: an empirical study of a software cost estimation modeling process in the defense domain

The primary focus of weapon systems research and development has moved from a hardware base to a software base and the cost of software development is increasing gradually. An accurate estimation of the cost of software development is now a very important task in the defense domain. However, existing models and tools for software cost estimation are not suitable for the defense domain due to problems of accuracy. Thus, it is necessary to develop cost estimation models that are appropriate to specific domains. Furthermore, most studies of methodology development are aligned with generic methodologies that do not consider the pertinent factors to specific domains, whereas new methodologies should reflect specific domains. In this study, we apply two generic methodologies to the development of a software cost estimation model, before suggesting an integrated modeling process specifically for the national defense domain. To validate our proposed modeling process, we performed an empirical study of 113 software development projects on weapon systems in Korea. A software cost estimation model was developed by applying the proposed modeling process. The MMRE value of this model was 0.566 while the accuracy was appropriate for use. We conclude that the modeling process and software cost estimation model developed in this study is suitable for estimating resource requirements during weapon system development in South Korea’s national defense domain. This modeling process and model may facilitate more accurate resource estimation by project planners, which will lead to more successful project execution.     

Safe and simple software cost analysis

Simple software cost-analysis methods are readily available, but they aren't always safe. The simplest method is to base your cost estimate on the typical costs or productivity rates of your previous projects. That approach will work well if your new project doesn't have any cost-critical differences from those previous projects, but it won't be safe if some critical cost driver has degraded. Simple history-based software cost-analysis methods would be safer if you could identify which cost driver factors were likely to cause critical cost differences and estimate how much cost difference would result if a critical cost driver changed by a given degree. In this article, I provide a safe and simple method for doing both of these by using some cost-estimating relationships. COCOMO II is an updated and re-calibrated version of COCOMO (COnstructive COst MOdel). I also show how the COCOMO II cost drivers let you perform cost sensitivity and tradeoff analyses, and discuss how you can use similar methods with other software cost-estimation models     

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.     

An empirical study of the effect of complexity, platform, and program type on software development effort of business applications

Several popular cost estimation models like COCOMO and function points use adjustment variables, such as software complexity and platform, to modify original estimates and arrive at final estimates. Using data on 666 programs from 15 software projects, this study empirically tests a research model that studies the influence of three adjustment variables—software complexity, computer platform, and program type (batch or online programs) on software effort. The results confirm that all the three adjustment variables have a significant effect on effort. Further, multiple comparison of means also points to two other results for the data examined. Batch programs involve significantly higher software effort than online programs. Programs rated as complex have significantly higher effort than programs rated as average.     

Quantifying IT estimation risks

A statistical method is proposed for quantifying the impact of factors that influence the quality of the estimation of costs for IT-enabled business projects. We call these factors risk drivers as they influence the risk of the misestimation of project costs. The method can effortlessly be transposed for usage on other important IT key performance indicators (KPIs), such as schedule misestimation or functionality underdelivery. We used logistic regression as a modeling technique to estimate the quantitative impact of risk factors. We did so because logistic regression has been applied successfully in fields including medical science, e.g. in perinatal epidemiology, to answer questions that show a striking resemblance to the questions regarding project risk management. In our study we used data from a large organization in the financial services industry to assess the applicability of logistic modeling in quantifying IT risks. With this real-world example we illustrated how to scrutinize the quality and plausibility of the available data. We explained how to deal with factors that cannot be influenced, also called risk factors, by project management before or in the early stage of a project, but can have an influence on the outcome of the estimation process. We demonstrated how to select the risk drivers using logistic regression. Our research has shown that it is possible to properly quantify these risks, even with the help of crude data. We discussed the interpretation of the models found and showed that the findings are helpful in decision making on measures to be taken to identify potential misestimates and thus mitigate IT risks for individual projects. We proposed increasing the auditing process efficiency by using the found cost misestimation models to classify all projects as either risky projects or non-risky projects. We discovered through our analyses that projects must not be overstaffed and the ratio of external developers must be kept small to obtain better cost estimates. Our research showed that business units that report on financial information tend to be risk mitigating, because they have more cost underruns in comparison with business units without reporting; the latter have more cost overruns. We also discovered a maturity mismatch: an increase from CMM level 1 to 2 did not influence the disparity between a cost estimate and its actual if the maturity of the business is not also increased.     

软件成本评估方法综述

如何做好软件项目预算一直是政府机关、企事业单位进行信息化建设的难题之一。软件成本评估是通过一套流程或模型对软件项目开发的工作量、工期和成本进行评估的行为,可以提高软件预算的精确度,有利于保障软件项目的交付周期,合理安排和调度研发人员。首先,对软件成本评估方法进行分类介绍和对比,分析其优缺点;然后,采用软件项目样本数据,对功能点、用例点、神经网络、类推4种评估方法进行实验分析;最后,指出现有的软件成本评估方法存在的问题和进一步研究的方向。     

Finding the right data for software cost modeling

Good software cost models can significantly help software project managers. With good models, project stakeholders can make informed decisions about how to manage resources, how to control and plan the project, or how to deliver the project on time, on schedule, and on budget. Real-world data sets, such as those coming from software engineering projects, often contain noisy, irrelevant, or redundant variables. We propose that cost modelers should perform data-pruning experiments after data collection and before model building. Such pruning experiments are simple and fast.     

Software effort models for early estimation of process controlapplications

Models are developed to estimate lines of code and function counts directly from user application features of process control systems early in the software development lifecycle. Since the application features are known with reasonable degree of confidence during early stages of development, it is possible to extend the use of the constructive cost model (COCOMO) and function-points-based approach for early software cost estimation. Alternative feature-based models that estimate size and effort using application features and productivity factors are developed. The feature-based models have been shown to estimate software effort with the least error     

Deploying realistic estimation (field situation analysis)

Realistic estimation is a process by which the cost and time of software projects can be predicted. This enables management to set up attainable project objectives—that is software development organizations delivering what was promised, on time, and in budget. The main benefit is an enhancement of the professional credibility of these organizations. I have observed that some organizational aspects support deployment of software estimation while others block it. In this paper, I have defined these aspects as Driving Forces and Restraining Forces, as per Kurt Lewin's Force-Field Analysis. The purpose of this paper is to review these elements with a view to provoking new thinking.     

The optimization of success probability for software projects using genetic algorithms

The software development process is usually affected by many risk factors that may cause the loss of control and failure, thus which need to be identified and mitigated by project managers. Software development companies are currently improving their process by adopting internationally accepted practices, with the aim of avoiding risks and demonstrating the quality of their work.This paper aims to develop a method to identify which risk factors are more influential in determining project outcome. This method must also propose a cost effective investment of project resources to improve the probability of project success.To achieve these aims, we use the probability of success relative to cost to calculate the efficiency of the probable project outcome. The definition of efficiency used in this paper was proposed by researchers in the field of education. We then use this efficiency as the fitness function in an optimization technique based on genetic algorithms. This method maximizes the success probability output of a prediction model relative to cost.The optimization method was tested with several software risk prediction models that have been developed based on the literature and using data from a survey which collected information from in-house and outsourced software development projects in the Chilean software industry. These models predict the probability of success of a project based on the activities undertaken by the project manager and development team. The results show that the proposed method is very useful to identify those activities needing greater allocation of resources, and which of these will have a higher impact on the projects success probability.Therefore using the measure of efficiency has allowed a modular approach to identify those activities in software development on which to focus the project's limited resources to improve its probability of success. The genetic algorithm and the measure of efficiency presented in this paper permit model independence, in both prediction of success and cost evaluation.     

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.     

A soft computing framework for software effort estimation

Accurate software estimation such as cost estimation, quality estimation and risk analysis is a major issue in software project management. In this paper, we present a soft computing framework to tackle this challenging problem. We first use a preprocessing neuro-fuzzy inference system to handle the dependencies among contributing factors and decouple the effects of the contributing factors into individuals. Then we use a neuro-fuzzy bank to calibrate the parameters of contributing factors. In order to extend our framework into fields that lack of an appropriate algorithmic model of their own, we propose a default algorithmic model that can be replaced when a better model is available. One feature of this framework is that the architecture is inherently independent of the choice of algorithmic models or the nature of the estimation problems. By integrating neural networks, fuzzy logic and algorithmic models into one scheme, this framework has learning ability, integration capability of both expert knowledge and project data, good interpretability, and robustness to imprecise and uncertain inputs. Validation using industry project data shows that the framework produces good results when used to predict software cost.     

一种嵌入式软件项目估计方法

软件项目估计是CMM2级软件项目策划KPA的基础,是软件开发中的一个重要环节。合理的估计是保证软件项目符合预算和进度要求的前提条件。描述了基于CMM的软件估计的过程,介绍了一种基于嵌入式软件项目的估计方法,并结合具体实例对规模估计、工作量／成本估计、关键计算机资源估计和进度估计的内容进行了细致的阐述。该研究为有效地规划和管理嵌入式软件项目,制定合理可行的软件开发计划提供了有力的支持。