Software cost estimation techniques presentation

Software cost estimation techniques
Presentation by Kudzai G. Rerayi B1542349

SOFTWARE COST ESTIMATION TECHNIQUES
 In the actual cost estimation process there are
other inputs and constraints that needed to be
considered besides the cost drivers.
 One of the primary constraints of the software
cost estimate is the financial constraint, which
are the amount of the money that can be
budgeted or allocated to the project. There are
other constraints such as manpower constraints,
and date constraints.

Expert judgement
 several experts on the proposed software development techniques
and the application domain are consulted.
 They each estimate the project cost. These estimates are
compared and discussed. The estimation process iterates until an
agreed estimate is reached.
 This technique captures the experience and the knowledge of the
estimator who provides the estimate based on their experience
from a similar project to which they have participated.

ADVANTAGES
 Relatively cheap estimation method.
 Can be accurate if experts have direct experience of similar systems
 Useful in the absence of quantified, empirical data.
 Can factor in differences between past project experiences and
requirements of the proposed project
 Can factor in impacts caused by new technologies, applications and
languages.
DISADVANTAGES
 Estimate is only as good expert’s opinion.
 It is very inaccurate if there are no experts!
 Hard to document the factors used by the experts

Pricing to win
the software cost is estimated to be whatever the
customer has available to spent on the project.
 The estimated effort depends on the customer’s
budget and not on the software functionality.
In other words the cost estimate is the price that is
necessary to win the contract or the project.

ADVANTAGES
 Often rewarded with the contract
DISADVANTAGES
 Time and money run out before the job is done
 The probability that the customer gets the system
he or she wants is small. Costs do not accurately
reflect the work required.

Estimation by analogy
 This technique is applicable when other projects in the
same application domain have been completed. The cost
of new project is estimated by analogy with these
completed projects.
 comparing the proposed project to previous completed
similar project in the same application domain.
 The actual data from the completed projects are
extrapolated. Can be used either at system or component
level.

ADVANTAGES
 Based on actual project data
 It is accurate if project data available
DISADVANTAGES
 Impossible if no comparable project had been
tackled in the past
 How well does the previous project represent this
one

Bottom up
cost of each software component is estimated
and then combine the results to arrive the total
cost for the project.
The goal is to construct the estimate of the
system from the knowledge accumulated about
the small software components and their
interactions.

ADVANTAGES
 More stable
 More detailed
 Allow each software group to hand an estimate
DISADVANTAGES
 May overlook system level costs
 More time consuming

TOP-DOWN
This technique is also called Macro Model,
which utilize the global view of the
product and then partitioned into various
low level components.
start at the system level and assess the
overall system functionality and how this
is delivered through sub systems

ADVANTAGES
 Requires minimal project detail
 Usually faster and easier to implement
 Focus on system level activities
DISADVANTAGES
 Tend to overlook low level components
 No detailed basis

Algorithmiccost modelling
use of mathematical equations to predict
cost estimations
Equations are based on theory or
historical data.
An algorithmic cost model can be built by
analyzing the costs and attributes of
completed projects and finding the
closest fit formula to actual experience.

ADVANTAGES
 No detailed basis
 Easy to modify input data
 Easy to refine and customize formulas
 Objectively calibrated to experience
DISADVANTAGES
 Unable to deal with exceptional conditions
 Some experience and factors can not be quantified
 Sometimes algorithms may be proprietary

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