QATestLab 21, Garmatna str., Kiev, Ukraine ph.: +38(044)277-66-61 http://qatestlab.com/ contact@qa-testlab.com
Fault distribution is very uneven for the majority of software, not depending on their size, functionality, implementation language and other features.
Much empirical evidence has accumulated over the years to support the socalled 80:20 principle. It states that 20% of the software elements are answerable for 80% of the troubles. Such problematic elements may commonly be described by specific estimation properties about their design, size, complexity, change history. Because of the uneven fault distribution among software elements, there is a huge need for risk identification methods to analyze these estimation data so that inspection,software testing and other quality assurance activities can be concentrated on such potentially highdefect elements. There are several risk detecting methods: •
treebased modeling
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traditional statistical analysis methods
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neural networks
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learning algorithms
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pattern matching methods
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principal component and discriminant analysis
These methods can be described by such features as: •
exactness
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presence of tool support
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ease of result interpretation
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simplicity
(c) QATestLab, 2011
http://qatestlab.com/
QATestLab 21, Garmatna str., Kiev, Ukraine ph.: +38(044)277-66-61 http://qatestlab.com/ contact@qa-testlab.com
•
stability
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creative info
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early presence
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manual for quality betterment
Correct risk detecting methods may be picked to fit specific application environments with the goal to detect highrisk software elements for focused inspection and software testing.
(c) QATestLab, 2011
http://qatestlab.com/