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QATestLab 21, Garmatna str., Kiev, Ukraine ph.: +38(044)277-66-61

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 

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: •


presence of tool support

ease of result interpretation


(c) QATestLab, 2011                     

QATestLab 21, Garmatna str., Kiev, Ukraine ph.: +38(044)277-66-61


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 high­risk software elements for focused inspection and software testing.

(c) QATestLab, 2011                     

Risk Identification Methods In Software Testing  

Fault distribution is very uneven for the majority of software, not depending on their size, functionality, implementation language and othe...

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