The document discusses four methods for predicting the number of defects remaining in software: 1. Tracking defects found during development, system testing, and deployment. 2. Modeling the number of defects based on factors like complexity, injection rate, and size. 3. Using reliability growth models like the Weibull distribution to plot defects over time/testing effort. 4. Dividing the software into cells to track defects found in each cell.

1. MOTOROLA SOLUTIONS
How many defects
are left?
© 2015 Motorola Solutions, Inc.
ARTUR GORSKI

2. How many defects
are left?
How many defects
are you going to
find?
© 2015 Motorola Solutions, Inc.

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METHOD 1

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Use Feedback

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Development System Tests Deployment
80

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Development System Tests Deployment
80 40

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Development System Tests Deployment
80 3040

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Development System Tests Deployment
80 3040

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Development System Tests Deployment
80 3040

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Defects found during System Tests
DefectsfoundduringDeployment

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DefectsfoundduringDeployment
Defects found during System Tests

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METHOD 2

17. Source of defects?
COMPLEXITY
INJECTION RATE
SIZE
© 2016 Motorola Solutions, Inc.

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Number of injected defects
Probability

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CumulativeNumberOfDefects
Iterations

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Iterations
CumulativeNumberOfDefects

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CumulativeNumberOfDefects
Iterations

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All models
are wrong,
but some
are useful
George E.P. Box
“

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METHOD 3

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Waloddi
Weibull
(Sweden)

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Metrics and Models in Software Quality Engineering, 2nd Edition [Chapter 7]
Software Reliability
Engineering

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CumulativeNumberOfDefects
Testing effort or Time

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CumulativeNumberOfDefects
Testing effort or Time

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METHOD 4

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Metrics and Models in Software Quality Engineering, 2nd Edition
Cell 1Cell 2
Cell 3 Cell 4

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Metrics and Models in Software Quality Engineering, 2nd Edition
Cell 1Cell 2
Cell 3 Cell 4

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Metrics and Models in Software Quality Engineering, 2nd Edition
Cell 1Cell 2
Cell 3 Cell 4

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Metrics and Models in Software Quality Engineering, 2nd Edition
Cell 1Cell 2
Cell 3 Cell 4

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Without data
you`re just
another person
with an opinion
W. Edwards Deming
“

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Start presenting state of quality with defects prediction.
@gorski_artur
Cell 3 Cell 4
Cell 2 Cell 1
artek.gorski@gmail.com