 | This article needs additional citations for verification .(October 2015) |
The Constructive Cost Model (COCOMO) is a procedural software cost estimation model developed by Barry W. Boehm. The model parameters are derived from fitting a regression formula using data from historical projects (63 projects for COCOMO 81 and 163 projects for COCOMO II).
The constructive cost model was developed by Barry W. Boehm in the late 1970s [1] and published in Boehm's 1981 book Software Engineering Economics [2] as a model for estimating effort, cost, and schedule for software projects. It drew on a study of 63 projects at TRW Aerospace where Boehm was Director of Software Research and Technology. The study examined projects ranging in size from 2,000 to 100,000 lines of code, and programming languages ranging from assembly to PL/I. These projects were based on the waterfall model of software development which was the prevalent software development process in 1981.
References to this model typically call it COCOMO 81. In 1995 COCOMO II was developed and finally published in 2000 in the book Software Cost Estimation with COCOMO II. [3] COCOMO II is the successor of COCOMO 81 and is claimed to be better suited for estimating modern software development projects; providing support for more recent software development processes and was tuned using a larger database of 161 projects. The need for the new model came as software development technology moved from mainframe and overnight batch processing to desktop development, code reusability, and the use of off-the-shelf software components.
COCOMO consists of a hierarchy of three increasingly detailed and accurate forms. The first level, Basic COCOMO is good for quick, early, rough order of magnitude estimates of software costs, but its accuracy is limited due to its lack of factors to account for difference in project attributes (Cost Drivers). Intermediate COCOMO takes these Cost Drivers into account and Detailed COCOMO additionally accounts for the influence of individual project phases. Last one is Complete COCOMO model which addresses the shortcomings of both basic & intermediate.
Intermediate COCOMO computes software development effort as function of program size and a set of "cost drivers" that include subjective assessment of product, hardware, personnel and project attributes. This extension considers a set of four "cost drivers", each with a number of subsidiary attributes:-
Each of the 15 attributes receives a rating on a six-point scale that ranges from "very low" to "extra high" (in importance or value). An effort multiplier from the table below applies to the rating. The product of all effort multipliers results in an effort adjustment factor (EAF). Typical values for EAF range from 0.9 to 1.4.
| Cost Drivers | Ratings | |||||
|---|---|---|---|---|---|---|
| Very Low | Low | Nominal | High | Very High | Extra High | |
| Product attributes | ||||||
| Required software reliability | 0.75 | 0.88 | 1.00 | 1.15 | 1.40 | |
| Size of application database | 0.94 | 1.00 | 1.08 | 1.16 | ||
| Complexity of the product | 0.70 | 0.85 | 1.00 | 1.15 | 1.30 | 1.65 |
| Hardware attributes | ||||||
| Run-time performance constraints | 1.00 | 1.11 | 1.30 | 1.66 | ||
| Memory constraints | 1.00 | 1.06 | 1.21 | 1.56 | ||
| Volatility of the virtual machine environment | 0.87 | 1.00 | 1.15 | 1.30 | ||
| Required turnabout time | 0.87 | 1.00 | 1.07 | 1.15 | ||
| Personnel attributes | ||||||
| Analyst capability | 1.46 | 1.19 | 1.00 | 0.86 | 0.71 | |
| Applications experience | 1.29 | 1.13 | 1.00 | 0.91 | 0.82 | |
| Software engineer capability | 1.42 | 1.17 | 1.00 | 0.86 | 0.70 | |
| Virtual machine experience | 1.21 | 1.10 | 1.00 | 0.90 | ||
| Programming language experience | 1.14 | 1.07 | 1.00 | 0.95 | ||
| Project attributes | ||||||
| Application of software engineering methods | 1.24 | 1.10 | 1.00 | 0.91 | 0.82 | |
| Use of software tools | 1.24 | 1.10 | 1.00 | 0.91 | 0.83 | |
| Required development schedule | 1.23 | 1.08 | 1.00 | 1.04 | 1.10 | |
The Intermediate Cocomo formula now takes the form:
where E is the effort applied in person-months, KLoC is the estimated number of thousands of delivered lines of code for the project, and EAF is the factor calculated above. The coefficient ai and the exponent bi are given in the next table.
| Software project | ai | bi | ci |
|---|---|---|---|
| Organic | 3.2 | 1.05 | 0.38 |
| Semi-detached | 3.0 | 1.12 | 0.35 |
| Embedded | 2.8 | 1.20 | 0.32 |
The Development time D and also the most effective number of Persons P calculation uses E in the same way as in the Basic COCOMO:
Note that in addition to the EAF, the parameter ai is different in Intermediate COCOMO from the Basic model:
| Software project | ab |
|---|---|
| Organic | 2.4 |
| Semi-detached | 3.0 |
| Embedded | 3.6 |
The parameters b and c are the same in both models.
Source lines of code (SLOC), also known as lines of code (LOC), is a software metric used to measure the size of a computer program by counting the number of lines in the text of the program's source code. SLOC is typically used to predict the amount of effort that will be required to develop a program, as well as to estimate programming productivity or maintainability once the software is produced.
Barry William Boehm was an American software engineer, distinguished professor of computer science, industrial and systems engineering; the TRW Professor of Software Engineering; and founding director of the Center for Systems and Software Engineering at the University of Southern California. He was known for his many contributions to the area of software engineering.
Cost estimation models are mathematical algorithms or parametric equations used to estimate the costs of a product or project. The results of the models are typically necessary to obtain approval to proceed, and are factored into business plans, budgets, and other financial planning and tracking mechanisms.
The Wideband Delphi estimation method is a consensus-based technique for estimating effort. It derives from the Delphi method which was developed in the 1950-1960s at the RAND Corporation as a forecasting tool. It has since been adapted across many industries to estimate many kinds of tasks, ranging from statistical data collection results to sales and marketing forecasts. According to the Project Management Institute, Wideband Delphi is an "estimating method in which subject matter experts go through multiple rounds of producing estimates individually, with a team discussion after each round, until a consensus is achieved."
The Constructive Systems Engineering Cost Model (COSYSMO) was created by Ricardo Valerdi while at the University of Southern California Center for Software Engineering. It gives an estimate of the number of person-months it will take to staff systems engineering resources on hardware and software projects. Initially developed in 2002, the model now contains a calibration data set of more than 50 projects provided by major aerospace and defense companies such as Raytheon, Northrop Grumman, Lockheed Martin, SAIC, General Dynamics, and BAE Systems.
SEER for Software (SEER-SEM) is a project management application used to estimate resources required for software development.
The function point is a "unit of measurement" to express the amount of business functionality an information system provides to a user. Function points are used to compute a functional size measurement (FSM) of software. The cost of a single unit is calculated from past projects.
The Putnam model is an empirical software effort estimation model. The original paper by Lawrence H. Putnam published in 1978 is seen as pioneering work in the field of software process modelling. As a group, empirical models work by collecting software project data and fitting a curve to the data. Future effort estimates are made by providing size and calculating the associated effort using the equation which fit the original data.
In project management, the Cone of Uncertainty describes the evolution of the amount of best case uncertainty during a project. At the beginning of a project, comparatively little is known about the product or work results, and so estimates are subject to large uncertainty. As more research and development is done, more information is learned about the project, and the uncertainty then tends to decrease, reaching 0% when all residual risk has been terminated or transferred. This usually happens by the end of the project i.e. by transferring the responsibilities to a separate maintenance group.
Programming productivity describes the degree of the ability of individual programmers or development teams to build and evolve software systems. Productivity traditionally refers to the ratio between the quantity of software produced and the cost spent for it. Here the delicacy lies in finding a reasonable way to define software quantity.
A parametric model is a set of related mathematical equations that incorporates variable parameters. A scenario is defined by selecting a value for each parameter. Software project managers use software parametric models and parametric estimation tools to estimate their projects' duration, staffing and cost.
In project management, accurate estimates are the basis of sound project planning. Many processes have been developed to aid engineers in making accurate estimates, such as
In software development, effort estimation is the process of predicting the most realistic amount of effort required to develop or maintain software based on incomplete, uncertain and noisy input. Effort estimates may be used as input to project plans, iteration plans, budgets, investment analyses, pricing processes and bidding rounds.
Daniel D. Galorath is an American software developer, businessman and author. Galorath is the President and CEO of Galorath Incorporated and one of the chief developers of the project management software known as SEER-SEM.
In software engineering, a software development process is a process of planning and managing software development. It typically involves dividing software development work into smaller, parallel, or sequential steps or sub-processes to improve design and/or product management. It is also known as a software development life cycle (SDLC). The methodology may include the pre-definition of specific deliverables and artifacts that are created and completed by a project team to develop or maintain an application.
PRICE Systems was founded in 1975 as a business within the RCA Corporation. It is generally acknowledged as the earliest developer of parametric cost estimation software.
Weighted Micro Function Points (WMFP) is a modern software sizing algorithm which is a successor to solid ancestor scientific methods as COCOMO, COSYSMO, maintainability index, cyclomatic complexity, function points, and Halstead complexity. It produces more accurate results than traditional software sizing methodologies, while requiring less configuration and knowledge from the end user, as most of the estimation is based on automatic measurements of an existing source code.
AFCAA REVIC is a set of programs for use in estimating the cost of software development projects. The Revised Enhanced Version of Intermediate COCOMO (REVIC) model is a copyrighted program available for public distribution under agreement with the REVIC developer, Ray Kile, and the U.S. Air Force Cost Analysis Agency (AFCAA). It is distributed by the U.K. Navy Engineering Process Office (EPO).
Methodology to evaluate complexity and cost of developing and maintaining application software for creating information systems (Method CETIN) - this is an algorithmic model assessment value software, developed by a consortium of Kazakh IT companies: Kazakhstan Association of IT-companies Company System Studies "Factor" and JSC "National Information Technologies. Method allows assessment of complexity and cost of development of [software] in the early stages and is based on an assessment of the functional size of software.
Object points are an approach used in software development effort estimation under some models such as COCOMO II.
DOC 