A Metric is a quantitative measure of the degree to which a system, component or process possesses a given attribute. Software metrics are measures that are used to quantify the software, software development resources and software development process. A metric is defined to be the name of a mathematical function used to measure some attribute of a product or process. The actual numerical value produced by a metric is a measure. For example, cyclomatic complexity is a metric; when applied to program code, the number yielded by the formula is the cyclomatic complexity measure.
Two general classes of metrics include the following:
- Management metrics , which assist in the management of the software development process.
- Quality metrics , which are predictors or indicators of the product qualities.
Metrics related to software error detection ("Testing") in the broad sense, grouped into the following categories:
General metrics that may be captured and analysed throughout the product life cycle
Software Requirements metrics , which may give early warning of quality problems in requirements specifications
Software Design metrics , which may be used to assess the status of software designs;
Code metrics reveal properties of the program source code;
Test metrics can be used to control the testing process, to assess its effectiveness, and to set improvement targets;
Software Installation metrics, which are applicable during the installation process;
Software Operation and Maintenance metrics , including those used in providing software product support.
Test Metrics
The following are the metrics collected in the testing process.
1.Defect age
Defect age is the time from when a defect is introduced to when it is detected (or fixed). Assign the numbers 1 through 6 to each of the software development activities from software requirements to software operation and maintenance. The defect age is computed as shown.
(Activity Detected - Activity Introduced)
Average Defect Age = -------------------
Number of Defects
2. Defect response time
This measure is the time between when a defect is detected to when it is fixed or closed.
3. Defect cost ($ d )
The cost of a defect may be computed as:
$ d = ( cost to analyse the defect) + (cost to fix it)
+ (cost of failures already incurred due to it)
4. Defect removal efficiency (DRE)
The DRE is the percentage of defects that have been removed during an activity, computed with the equation below. The DRE can also be computed for each software development activity and plotted on a bar graph to show the relative defect removal efficiencies for each activity. Or, the DRE may be computed for a specific task or technique (e.g., design inspection, code walkthrough, unit test, 6 month operation, etc.). [SQE]
Number Defects Removed
DRE = ------------------- * 100
Number Defects At Start Of Process
5 Mean time to failure (MTTF)
Gives an estimate of the mean time to the next failure, by accurately recording failure times t i , the elapsed time between the ith and the (i-1)st failures, and computing the average of all the failure times. This metric is the basic parameter required by most software reliability models. High values imply good reliability.
MMTF should be corrected by a weighted scheme similar to that used for computing Fault density (see under Test Metrics).
6 . Fault density (FD)
This measure is computed by dividing the number of faults by the size (usually in
KLOC, thousands of lines of code).
