Defect Density

Purpose:
Defect Density is used to detect the rate at which developers are generating defects

Definition
Defect Density is the number of confirmed defects detected in software/module during a defined period of development divided by the size of the software/module.

Formula
Defect Density = Defect/unit size

Unit size=Size is typically counted either in Lines of Code or Function Points.

Comments

Post a Comment

Popular posts from this blog

Data Migration Testing Startegy

Image
1.   Purpose of the Article The purpose of this Aricle is to describe the different test stages that will be performed; how the testing will be prepared and executed for each stage, and the criteria that need to be met before transition from one stage to the next can occu r. 2.   Phases involved in Data Migration Testing Below are the phases involved in testing data migration. ·         Fusion ·         Release 1.       Design 2.       Implementation a)       Ear b)       Iterations 3.       SIT (System Integration Testing) 4.       UAT (User Acceptance Testing) 2.1.         Fusion (Discover and Define) In this Phase the requirements for the...
Read more

Informatica DVO

What is Data Validation Option ? What can be tested ? To answer above two questions, Data Validation Option is a ETL Testing tool which comes along with power center and enables to test and validate data Data Validation Option reads table definitions from Power Center metadata repositories, and checks the data for inconsistencies Verify that moved or transformed data is complete and accurate and has not been changed because of errors in the movement or transformation process Will not check the correctness of transformations or mappings designed in Informatica Data Validation stages A typical workflow or stages for data validation consists of multiple tasks- 1. Data Validation Option reads one or more Power Center metadata repositories 2. We define the validation rules(creation of tests) in Data Validation Option 3. We run the rules(tests) to ensure the data conforms to the validation rules. When we do this, Data  Validation Option performs the following tasks: ...
Read more

ETL Testing

Image
Data ware house is relational database which is subject oriented, integrated, time-variant and nonvolatile collection of data used to support strategic decision making process Subject Oriented Data warehouses are designed to help you analyze data. For example, to learn more about your company's sales data, you can build a warehouse that concentrates on sales. Using this warehouse, you can answer questions like "Who was our best customer for this item last year?" This ability to define a data warehouse by subject matter, sales in this case, makes the data warehouse subject oriented. Integrated Integration is closely related to subject orientation. Data warehouses must put data from disparate sources into a consistent format. They must resolve such problems as naming conflicts and inconsistencies among units of measure. When they achieve this, they are said to be integrated. Nonvolatile Nonvolatile means that, once entered into the warehouse, data should not cha...
Read more