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Object-oriented software development effort prediction using design patterns from object interaction analysisAdekile, Olusegun 15 May 2009 (has links)
Software project management is arguably the most important activity in modern
software development projects. In the absence of realistic and objective management, the
software development process cannot be managed in an effective way. Software
development effort estimation is one of the most challenging and researched problems in
project management. With the advent of object-oriented development, there have been
studies to transpose some of the existing effort estimation methodologies to the new
development paradigm. However, there is not in existence a holistic approach to
estimation that allows for the refinement of an initial estimate produced in the
requirements gathering phase through to the design phase. A SysML point methodology
is proposed that is based on a common, structured and comprehensive modeling
language (OMG SysML) that factors in the models that correspond to the primary phases
of object-oriented development into producing an effort estimate. This dissertation
presents a Function Point-like approach, named Pattern Point, which was conceived to
estimate the size of object-oriented products using the design patterns found in object
interaction modeling from the late OO analysis phase. In particular, two measures are proposed (PP1 and PP2) that are theoretically validated showing that they satisfy wellknown
properties necessary for size measures.
An initial empirical validation is performed that is meant to assess the usefulness
and effectiveness of the proposed measures in predicting the development effort of
object-oriented systems. Moreover, a comparative analysis is carried out; taking into
account several other size measures. The experimental results show that the Pattern Point
measure can be effectively used during the OOA phase to predict the effort values with a
high degree of confidence. The PP2 metric yielded the best results with an aggregate
PRED (0.25) = 0.874.
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