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Error Removal Effectiveness Metrics

Overview

In this tutorial, we will discuss two important metrics for error removal effectiveness. Provided below are sample error distribution and sample weights of the different classifications of the errors.

  • DERE
  • DWERE

Let’s assume a Sample hypothetical project called  TestingDocs with 325 KLOC and the development effort hours of 540 man-hours. The Error distribution is given below.

Errors detected Distribution: Table 1

Errors Code Testing Development

Sample Error Weights: Table 2

The sample defect weights in the project are as below. There are classified into three categories called

Simple, Average, and Complex.

Sample Defect Weights

 

DERE(Development Errors Removal Effectiveness)

DERE =\huge \frac{NDE}{(NDE + NYF)}

 

NDE = Number of Design & Development errors

NYF = Number of software failures in 1 year of Maintenance

Let’s calculate the metric for the Sample project based on the given data. Lookup the above table 1 for the number of design and development errors detected during those phases.

NDE = \huge 15

NYF = Number of software failures in 1 year of Maintenance = \huge 18

DERE = \huge \frac{15}{(15 + 18)}

 

= \huge \frac{15}{33}   =  \huge 0.45

 

DERE DWERE Software Metrics

DWERE(Development  Weighted Errors Removal Effectiveness)

 

DWERE = \huge \frac{WDE}{(WDE + WYF)}

 

WDE=The Weighted Development&Design Errors.

WYF = The Weighted number of software failures in 1 year of Maintenance.

Lookup both the tables 1 and 2 for the errors in the classifications and multiply with the corresponding defect weights to get the weighted score.

WDE = \huge 6*(2) + 7*(5) + 2*(10) = 12 + 35 + 20

= \huge 67

 

WYF = \huge 9*(2) + 6*(5) + 3*(10) = 18 + 30 + 30

= \huge 78

 

DWERE = \huge \frac{67}{(67 + 78)}

 

= \huge \frac{67}{145}

 

= \huge 0.46

 

Error Density Metrics

https://www.testingdocs.com/error-density-metrics/

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