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Software Engineering Metrics for specification Quality Metrics are used to access the quality of the analysis model n r =n f +n nf WHERE n r= Total No of requirements n f= Total Functional requirements n nf= Total Non functional requirements

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Software Engineering To determine the lack of ambiguity of requirements we have Q 1 =n ui /n r where n ui= total no of requirements which are agreed upon n r= Total No of requirements The closer the value to 1, the lower the ambiguity of the specification

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Software Engineering Completeness of requirements Q 2 = n u /(n i *n s ) WHERE n u= Is the no of unique functional requirements same as functional requirements n i= No of inputs n s= No of states It ignores non functional requirements

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Software Engineering Completeness Q 3 = n c /(n c +n nv ) Where n c= No of requirements that have been validated same as n ui n nv= No of requirements that have not been validated

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Software Engineering High level design matrices These metrics focus on the characteristics of the program architecture with the emphasis on the architectural structure and the effectiveness of the module Structural Complexity S(i)= f 2 out (i)

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Software Engineering Data Complexity D(i)= v(i)/[f out (i) +1] Where V(I)= No of inputs and outputs variables that are passed to and from module

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Software Engineering System complexity C(i)= S(i)+D(i) As each of these complexities increase the over all architectural complexities of the system increases. So that integration and testing also increase

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Software Engineering Another complexity metric HMK= length(i)* [f in (i)+f out (i)] 2 Where Length(i) is the of programming language statements in module i

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Software Engineering Fenton suggests a number of simple shape metrics that enable different programs architecture to be compared size= n+a Where n= is the no. of nodes a= no. of arcs depth= The longest path from the root node to the leaf node Width= Maximum no of nodes at any level Arc to node ratio r=a/n

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Software Measurement & Metrics

Software Measurement & Metrics

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