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Fault distribution is very uneven for the majority of software, not depending on their size, functionality, implementation language and other features.
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QATestLab21, Garmatna str., Kiev, Ukraineph.: +38(044)277-66-61http://qatestlab.com/ [email protected]
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 so-called 80:20 principle. It states that 20% of the software elements are answerable for 80% of the trou-bles.
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,soft-ware testing and other quality assurance activities can be concentrated on such poten-tially high-defect elements.
There are several risk detecting methods:
tree-based modeling traditional statistical analysis methods neural networks learning algorithms pattern matching methods principal component and discriminant analysis
These methods can be described by such features as:
exactness presence of tool support
(c) QATestLab, 2011 http://qatestlab.com/
QATestLab21, Garmatna str., Kiev, Ukraineph.: +38(044)277-66-61http://qatestlab.com/ [email protected]
ease of result interpretation simplicity stability creative info early presence manual for quality betterment
Correct risk detecting methods may be picked to fit specific application environments with the goal to detect high-risk software elements for focused inspection and software testing.
(c) QATestLab, 2011 http://qatestlab.com/
