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Computing optimal cocomo effort multiplier values and optimal casebase subsets using monte carlo methods

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Date Issued:
1996
Abstract/Description:
University of Central Florida College of Engineering Thesis; There have been many studies performed and techniques applied to solve the problem of estimating man-month effort for software projects. Despite all the effort expended to solving this problem the results achieved from the various techniques have not been embraced by the software community as very reliable or accurate. This thesis uses Monte Carlo methods to obtain optimal values for COCOMO effort multipliers which minimize the average of the absolute values of the relative errors (AARE) of man-month estimate for two industry supplied casebases. For example, when using three COCOMO cost drivers (complexity, language experience, application experience) and the COCOMO effort multiplier values, AARE values were 60% for casebase 1 and 53% for casebase 2; using Monte Carlo to obtain optimal effort multiplier values, AARE values were 34% for casebase 1 and 41% for casebase 2. By repeatedly removing the cases which contributed the greatest Absolute Relative Error, the Monte Carlo method was also used to determine optimal casebase subsets with AARE values of less than 10%. This latter approach identifies casebase cases for which the cost drivers may have been rated incorrectly or cases which are not rated consistently with respect to a subset of cases.
Title: Computing optimal cocomo effort multiplier values and optimal casebase subsets using monte carlo methods.
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Name(s): Maidhof, Robert Joseph, Author
Linton, Darrell G., Committee Chair
Engineering, Degree Grantor
Type of Resource: text
Date Issued: 1996
Publisher: University of Central Florida
Language(s): English
Abstract/Description: University of Central Florida College of Engineering Thesis; There have been many studies performed and techniques applied to solve the problem of estimating man-month effort for software projects. Despite all the effort expended to solving this problem the results achieved from the various techniques have not been embraced by the software community as very reliable or accurate. This thesis uses Monte Carlo methods to obtain optimal values for COCOMO effort multipliers which minimize the average of the absolute values of the relative errors (AARE) of man-month estimate for two industry supplied casebases. For example, when using three COCOMO cost drivers (complexity, language experience, application experience) and the COCOMO effort multiplier values, AARE values were 60% for casebase 1 and 53% for casebase 2; using Monte Carlo to obtain optimal effort multiplier values, AARE values were 34% for casebase 1 and 41% for casebase 2. By repeatedly removing the cases which contributed the greatest Absolute Relative Error, the Monte Carlo method was also used to determine optimal casebase subsets with AARE values of less than 10%. This latter approach identifies casebase cases for which the cost drivers may have been rated incorrectly or cases which are not rated consistently with respect to a subset of cases.
Identifier: CFR0011954 (IID), ucf:53104 (fedora)
Note(s): 1996-12-01
M.S.
Electrical and Computer Engineering
Masters
This record was generated from author submitted information.
Electronically reproduced by the University of Central Florida from a book held in the John C. Hitt Library at the University of Central Florida, Orlando.
Subject(s): Engineering -- Dissertations
Academic
Dissertations
Academic -- Engineering
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFR0011954
Restrictions on Access: public
Host Institution: UCF

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