# Error Removal Effectiveness Metrics

Document Contents

### 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 ### 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. ### DERE(Development Errors Removal Effectiveness)

DERE = 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 = NYF = Number of software failures in 1 year of Maintenance = DERE = = = ### DWERE(Development  Weighted Errors Removal Effectiveness)

DWERE = 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 = = WYF = = DWERE = = = Error Density Metrics

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