Reducing Estimation Uncertainty with Continuous Assessment: Tracking the “Cone of Uncertainty”

Keywords cost estimation Uncertainty Reducing Estimation Uncertainty with Continuous Assessment: Tracking the “Cone of Uncertainty”
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Our goal is to develop a routine, semi-automated assessment
framework that helps reduce uncertainties of the software project
estimation as the project progresses through its life cycle. The
assessment framework integrates the Unified Code Count tool
(UCC) developed by USC with the COCOMO II estimation
model to quickly generate information to analyze the team’s
performance and estimations. This is similar to the concepts of
[10], which shows that frequent assessment of the project status
help improve the team as well as the final product of the project.

We apply this concept to assess the efforts spent on the project
and compare with the current progress to predict the effort
required to complete the project. This information is then used to
evaluate the current project estimations and adjust the estimation
parameters as necessary. This will eventually enable the actual
and estimated effort to converge. The assessment framework
allows the team to validate the direction of the project, while
increasing the project understanding as well.

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technical white paper
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Accurate software cost and schedule estimations are essential
especially for large software projects. However, once the required
efforts have been estimated, little is done to recalibrate and reduce
the uncertainty of the initial estimates. To address this problem,
we have developed and used a framework to continuously
monitor the software project progress and read just the estimated
effort utilizing the Constructive Cost Model II (COCOMO II) and
the Unified CodeCount Tool developed by the University of
Southern California (USC). As a software project progresses, we
gain more information about the project itself, which can then be
used to assess and re-estimate the effort required to complete the
project. With more accurate estimations and less uncertainties, the
quality and goal of project outcome can be assured within the
available resources. The paper thus also provides and analyzes
empirical data on how projects evolve within the familiar
software “cone of uncertainty.”

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