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to determine if there is enough time and programming and testing resources avail-
able to reduce the defect backlog to zero before the development due date.
The metrics used to track the incremental discovery and correction of software
defects can be leveraged for valuable test analysis beyond one-by-one defect review
and backlog analysis. Consider the cost/benefi t tradeoff from adding one more col-
umn of information to the defect tracking record: the identifi cation of the code that
contained the defect.
This augmented defect tracking log can be used for root cause analysis. There
are a number of good statistical textbooks available that provide the mathematical
basis and practical application of root cause analysis to software defect logs. The
simplifi ed explanation of root cause analysis for testing is the mathematical search
for the “buggiest” code. This search can be accomplished by a simple frequency
count of corrected defect code earmarks, ordered with the most frequently occurring
code earmarks fi rst.
There is no current way to precisely predict beforehand how many or what kinds
of defects your test team will discover in a new software. Depending on the cir-
cumstances and maturity of the software development organization, predevelopment
defect predictions can range from an educated guess to reasonably accurate predic-
tions. If there is project history either from similar software applications or from
previous releases of the same software application, then that project history can be
leveraged for next project defect predictions.
KEY CONCEPTS
Attempted versus
unattempted test cases
Business risk to not
correct
Code earmarks
Customer defect
discovery correlation
Defect backlog
Defect log discovery
curve
Successful versus
unsuccessful test cases
Rayleigh curve
Root cause analysis
Severity codes
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