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。     

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.     

An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation

In project management, effective cost estimation is one of the most crucial activities to efficiently manage resources by predicting the required cost to fulfill a given task. However, finding the best estimation results in software development is challenging. Thus, accurate estimation of software development efforts is always a concern for many companies. In this paper, we proposed a novel software development effort estimation model based both on constructive cost model II (COCOMO II) and the artificial neural network (ANN). An artificial neural network enhances the COCOMO model, and the value of the baseline effort constant A is calibrated to use it in the proposed model equation. Three state-of-the-art publicly available datasets are used for experiments. The backpropagation feedforward procedure used a training set by iteratively processing and training a neural network. The proposed model is tested on the test set. The estimated effort is compared with the actual effort value. Experimental results show that the effort estimated by the proposed model is very close to the real effort, thus enhanced the reliability and improving the software effort estimation accuracy.     

A study of several metrics for programming effort

As the cost of programming becomes a major component of the cost of computer systems, it becomes imperative that program development and maintenance be better managed. One measurement a manager could use is programming complexity. Such a measure can be very useful if the manager is confident that the higher the complexity measure is for a programming project, the more effort it takes to complete the project and perhaps to maintain it. Until recently most measures of complexity were based only on intuition and experience. In the past 3 years two objective metrics have been introduced, McCabe's cyclomatic number v(G) and Halstead's effort measure E. This paper reports an empirical study designed to compare these two metrics with a classic size measure, lines of code. A fourth metric based on a model of programming is introduced and shown to be better than the previously known metrics for some experimental data.     

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.     

COCOMO as a schedule prognosis and validation tool: a case study

This paper describes a case study in the use of the COCOMO cost estimation model as a tool to provide an independent prognosis and validation of the schedule of a software project at IBM UK Laboratories Ltd, Hursley. Clearly case studies have the danger of being anecdotal however software engineers often work in situations where sufficient historical data is not available to calibrate models to the local environment. It is often necessary for the software engineer to attempt to use such tools on individual projects to justify their further use. This case study describes how we began to use COCOMO and concentrates on some of the problems and benefits which were encountered when trying to use COCOMO in a live development environment.The paper begins by discussing some problems in mapping the COCOMO phases on to the IBM development process. The practical aspects of gathering the development parameters of the model are described and the results of the work are presented in comparison to a schedule assessment using other prognosis techniques and the planned schedule at other milestones in the project's history. Some difficulties experienced in interpreting the data output from the model are discussed. This is followed by a brief comparison with other schedule analysis techniques used in quality assurance. We hope this case study shows that despite the problems in trying to use models such as COCOMO there are significant benefits in helping the user understand what is required to use such tools more effectively to improve software development cost estimates in the future.     

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     

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.     

军用型号项目软件复用成本度量模型

针对军用型号项目软件研制过程中普遍存在的软件复用行为,提出了一种复用成本度量方法,对传统的COCOMO2.0成本度量模型进行了改进,采用改进的功能点法估计软件实际规模,适当调整模型中的评估项,增加了度量系统复用的成本驱动因子及系统通用特性统计项（GSC）,建立了相应的量化评估及DI分级表,形成了军用型号项目软件进度、成本估计模型,使用改进的度量模型对某军用型号项目进行了成本度量,验证了方法的有效性。     

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.     

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.     

Dynamic project performance estimation by combining static estimation models with system dynamics

Changes in user requirements or project personnel occur frequently during project execution particularly in long-term and large-size projects. We need a tool which can estimate the effects of changing conditions to effectively manage the project.This paper proposes a simulation method for dynamic project performance in terms of effort, schedule, and defect density changes in a dynamic project environment by combining COCOMO II with system dynamics. We apply expert judgment technique to overcome the lack of empirical data on the effects of dynamic project environment. We develop a simulation tool (available on the authors’ website) which has model adjustment parameters to reflect experts’ estimation on project characteristics. The simulation experiment on a military application development project demonstrates that the developed model can show the behavioral characteristics of a project suffering unanticipated and uncontrolled requirements creep. This helps project managers understand interactions between project factors and proactively evaluate and control the effects of dynamic project environment.     

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.     

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     

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     

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.     

Identifying factors affecting software development cost and productivity

Software systems of today are often complex, making development costs difficult to estimate. This paper uses data from 50 projects performed at one of the largest banks in Sweden to identify factors that have an impact on software development cost. Correlation analysis of the relationship between factor states and project costs was assessed using ANOVA and regression analysis. Ten out of the original 31 factors turned out to have an impact on software development project cost at the Swedish bank including the: number of function points, involved risk, number of budget revisions, primary platform, project priority, commissioning body’s unit, commissioning body, number of project participants, project duration, and number of consultants. In order to be able to compare projects of different size and complexity, this study also considers the software development productivity defined as the amount of function points per working hour in a project. The study at the bank indicates that the productivity is affected by factors such as performance of estimation and prognosis efforts, project type, number of budget revisions, existence of testing conductor, presentation interface, and number of project participants. A discussion addressing how the productivity factors relate to cost estimation models and their factors is presented. Some of the factors found to have an impact on cost are already included in estimation models such as COCOMO II, TEAMATe, and SEER-SEM, for instance function points and software platform. Thus, this paper validates these well-known factors for cost estimation. However, several of the factors found in this study are not included in established models for software development cost estimation. Thus, this paper also provides indications for possible extensions of these models.     

中等COCOMO模型在中小型软件项目投资决策中的应用探讨

中等COCOMO模型是经过实际软件项目验证和修正的软件成本估算模型.文章将中等COCOMO模型应用于中小型软件项目投资决策,提出了一套简便而完整的中小型软件项目投资分析方法.     

Preliminary Data Analysis Methods in Software Estimation

Software is quite often expensive to develop and can become a major cost factor in corporate information systems budgets. With the variability of software characteristics and the continual emergence of new technologies the accurate prediction of software development costs is a critical problem within the project management context. In order to address this issue a large number of software cost prediction models have been proposed. Each model succeeds to some extent but they all encounter the same problem, i.e., the inconsistency and inadequacy of the historical data sets. Often a preliminary data analysis has not been performed and it is possible for the data to contain non-dominated or confounded variables. Moreover, some of the project attributes or their values are inappropriately out of date, for example the type of computer used for project development in the COCOMO 81 (Boehm, 1981) data set. This paper proposes a framework composed of a set of clearly identified steps that should be performed before a data set is used within a cost estimation model. This framework is based closely on a paradigm proposed by Maxwell (2002). Briefly, the framework applies a set of statistical approaches, that includes correlation coefficient analysis, Analysis of Variance and Chi-Square test, etc., to the data set in order to remove outliers and identify dominant variables. To ground the framework within a practical context the procedure is used to analyze the ISBSG (International Software Benchmarking Standards Group data—Release 8) data set. This is a frequently used accessible data collection containing information for 2,008 software projects. As a consequence of this analysis, 6 explanatory variables are extracted and evaluated.