We have derived a simple equation that can be used to more accurately estimate the time needed to implement a set of R software requirements given an average Defect ratio . The equation results can also be used to adjust the development team work schedule, the number of developers, or the project number of software requirements.

1. The Non-intuitive Impact of Software Defects
on Development Efforts Time Estimates
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2. Motivation
• Many software development projects rely on software
implementation time estimates provided by the
development team to forecast the total duration of a
software implementation effort.
• This presentation takes a rational look at the non-intuitive
impact of software defects on development time estimates.
• We have derived a simple equation that can be used to
more accurately estimate the time needed to implement a
set of R software requirements .
• The equation results can also be used to adjust the
development team work schedule, the number of
developers, or the project number of software
requirements to implement.
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3. Equation Variables
• R = number of requirements that need to be
implemented in the software
• I = mean implementation rate of software
requirements. I unit is in Requirements per time
unit (for example 23 requirements per week)
• D = mean implemented requirements defect
ratio. For example if D=20% then for every 10
implemented requirements, 2 of them are not
properly implemented.
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4. Defect Ratio
• Many factors influence the value of the Defect
ratio such as
– Misunderstanding of requirements from the
designer and/or developer resulting in software
behavior that does not fully fulfill the
requirements
– Errors in software code
• The reader can refer to the Wikipedia article
on software bug for more details.
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5. Defect Ratio
• The Defect ratio we are talking about is an
average value.
• This average (or mean) value can be
determined through historical metrics
gathered by the software development
organization or team over an appropriate
timespan and over a set of past development
efforts.
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6. Defect Ratio
• The Defect ratio can also be determined as the
probability that an implemented requirement
generates a defect at any point in time during
an entire software development project.
• The reader can refer to our SlideShare
presentation “The dynamic interaction of
passed and failed requirements during
software testing” for further details on how to
calculate such probabilities.
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7. The Equation
• T = time needed to implement a set of R
software requirements. It is given by this
equation [E]:
𝑅
𝑇=
𝐼(1 − 𝐷)
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8. Equation Interpretation
• When the defect ratio is D = 0 then the time
needed to implement R software requirements is:
𝑅
𝑇=
𝐼
• “I (1-D)” represents an adjusted Implementation
rate that decreases in direct proportion to the
defect ratio. It captures the effect of rework
efforts that slow down production throughput.
• As the defect ratio varies from 0 to 1 , the time
needed to implement the set of R software
requirements increases in a non-linear way.
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9. Equation Illustration
• Assume that R=100 requirements need to be
implemented at a rate I=10 requirements per
week
• The chart of T (time needed to implement the
100 requirements) as a function of D
(implemented requirements defect ratio) is
presented on the next slide
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10. Equation Chart
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11. Equation Illustration
• In this example, when D=0 it takes 10 weeks
to implement the 100 requirements. This is
the common way of estimating
implementation time where defects rework is
not taken into account.
• Equation [E] explicitly incorporates the impact
of defects on the total time needed for
implementing the set of R requirements.
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12. Non-intuitive Impact of Defects
• With a 20% Defect ratio, the implementation
time of the 100 requirements goes from 10
weeks to 12.5 weeks which is a 25% increase.
• A 30% Defect ratio results in 42% increase in
implementation time: T goes from 10 weeks
to 14.2 weeks!
• A 50% defect ratio results in 100% increase in
implementation time: T goes from 10 weeks
to 20 weeks!
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13. Impact of High Defect Ratios
• As the Defect ratio approaches 100%, the
Implementation time grows at a faster rate
towards infinity.
• This behavior can be intuitively understood if one
considers that with 100% defect nothing gets
properly implemented and every requirement
needs to be re-implemented over and over again
leading to an infinite implementation time.
• What is not so easily understood is the non-linear
increasing rate of growth in Implementation time
as the Defect ratio increases.
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14. Impact on Software Requirements
Implementation Rate
• Lets examine again the case where the Defect
ratio is D=0%. It would take 10 weeks to
implement the 100 requirements at a rate of 10
requirements/week.
• If the defect ratio is actually 30%, it now takes
14.2 weeks to implement the 100 requirements.
To implement the 100 requirements in the initial
10 weeks, the Implementation rate must
increase. Using [E]:
 T = 10 = 100/(I x (1-0.3)
 I = 100/(10 x 0.7) = 14.2 requirements/week
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15. Impact on Software Requirements
Implementation Rate
• An implementation rate of I = 14.2
requirements/week can be obtained in several
ways:
– If the development team remains the same size,
then we must increase its weekly hours by 42%
– If weekly hours cannot be increased then we must
increase the size of the development team by
42%, so if we had initially 5 developers we now
need 2 more developers to have 7 developers!
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16. Impact on Software Requirements Size
• With the Defect ratio D=30%, if we had to
keep the same development team size, the
same implementation rate, and the same
implementation time, then we must reduce
the number of requirements from 100 to:
 T = 10 = R /(10 x (1-0.3)
 R = 10 x 10 x 0.7 = 70 Requirements!
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17. For Comments and Questions contact didier@pragmaticohesion.com
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