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Wallander, J. (2013) Predicting complexity of GUI intensive web apps - Building basic prediction models to estimate the complexity of web apps developed using two frameworks. Göteborg : Chalmers University of Technology
BibTeX
@mastersthesis{
Wallander2013,
author={Wallander, Jonas},
title={Predicting complexity of GUI intensive web apps - Building basic prediction models to estimate the complexity of web apps developed using two frameworks},
abstract={In this thesis two proprietary frameworks are analyzed in order to determine the complexity
of apps created with these two frameworks. Previously developed applications are
investigated to determine their complexity in form of source lines of code and function
points. The frameworks used to develop applications provide different building blocks,
and the building blocks are identified by visual observations of existing applications.
Once the building blocks are identified they are analyzed in isolation to determine
their complexity, and the outcome is used to produce two basic prediction models for
determining development complexity of future applications. The prediction models are
validated by implementing an example application using each framework, measuring
the complexity of implemented example applications and comparing it to the predicted
complexity.
A survey is performed with the target group of the prediction models, prior to announcing
any results, and the outcome of the questionnaire indicated that it is not
believed to be a linear relation between complexity of an application and the time it
takes to implement it, and that a prediction model should have an accuracy of at least
25%.
The prediction models proposed for the two frameworks are not deemed accurate
enough. Accuracy indicators, measured against data outside of the training set, ranges
between 30% and 78% for framework A and between 37% and 94% for framework B.
The accuracy of the prediction models obtained by cross-validation ranges between 42%
and 166% for framework A, and between 62% and 353% for framework B. The proposed
prediction models, as they are, should only be used to get a perception of the complexity
at hand of a suggested application. More data is needed to reduce the magnitude of error
and to be able to draw any statistically significant conclusions about the estimates.},
publisher={Institutionen för data- och informationsteknik (Chalmers), Chalmers tekniska högskola},
place={Göteborg},
year={2013},
note={71},
}
RefWorks
RT Generic
SR Print
ID 217064
A1 Wallander, Jonas
T1 Predicting complexity of GUI intensive web apps - Building basic prediction models to estimate the complexity of web apps developed using two frameworks
YR 2013
AB In this thesis two proprietary frameworks are analyzed in order to determine the complexity
of apps created with these two frameworks. Previously developed applications are
investigated to determine their complexity in form of source lines of code and function
points. The frameworks used to develop applications provide different building blocks,
and the building blocks are identified by visual observations of existing applications.
Once the building blocks are identified they are analyzed in isolation to determine
their complexity, and the outcome is used to produce two basic prediction models for
determining development complexity of future applications. The prediction models are
validated by implementing an example application using each framework, measuring
the complexity of implemented example applications and comparing it to the predicted
complexity.
A survey is performed with the target group of the prediction models, prior to announcing
any results, and the outcome of the questionnaire indicated that it is not
believed to be a linear relation between complexity of an application and the time it
takes to implement it, and that a prediction model should have an accuracy of at least
25%.
The prediction models proposed for the two frameworks are not deemed accurate
enough. Accuracy indicators, measured against data outside of the training set, ranges
between 30% and 78% for framework A and between 37% and 94% for framework B.
The accuracy of the prediction models obtained by cross-validation ranges between 42%
and 166% for framework A, and between 62% and 353% for framework B. The proposed
prediction models, as they are, should only be used to get a perception of the complexity
at hand of a suggested application. More data is needed to reduce the magnitude of error
and to be able to draw any statistically significant conclusions about the estimates.
PB Institutionen för data- och informationsteknik (Chalmers), Chalmers tekniska högskola,
LA eng
OL 30
