Software Test Metrics

Software Test Metrics
I) Introduction

When we can measure what we are speaking about and express it in numbers, we know something about it; but when we cannot measure, when we cannot express it in numbers, our knowledge is of a meager and unsatisfactory kind: it may be the beginning of knowledge, but we have scarcely, in your thoughts, advanced to the stage of science.

Why we need Metrics?

“We cannot improve what we cannot measure.”
“We cannot control what we cannot measure”


o    Take decision for next phase of activities
o    Evidence of the claim or prediction
o    Understand the type of improvement required
o    Take decision on process or technology change

II) Type of metrics
Base Metrics (Direct Measure)

Base metrics constitute the raw data gathered by a Test Analyst throughout the testing effort.  These metrics are used to provide project status reports to the Test Lead and Project Manager; they also feed into the formulas used to derive Calculated Metrics.

Ex: # of Test Cases, # of Test Cases Executed

Calculated Metrics (Indirect Measure)

Calculated Metrics convert the Base Metrics data into more useful information.  These types of metrics are generally the responsibility of the Test Lead and can be tracked at many different levels (by module, tester, or project).

Ex: % Complete, % Test Coverage

Base Metrics & Test Phases

o    # of Test Cases (Test Development Phase)
o    # of Test Cases Executed (Test Execution Phase)
o    # of Test Cases Passed (Test Execution Phase)
o    # of Test Cases Failed (Test Execution Phase)
o    # of Test Cases Under Investigation (Test Development Phase)
o    # of Test Cases Blocked (Test dev/execution Phase)
o    # of Test Cases Re-executed (Regression Phase)
o    # of First Run Failures (Test Execution Phase)
o    Total Executions (Test Reporting Phase)
o    Total Passes (Test Reporting Phase)
o    Total Failures (Test Reporting Phase)
o    Test Case Execution Time ((Test Reporting Phase)
o    Test Execution Time (Test Reporting Phase

Calculated Metrics & Phases

The below metrics are created at Test Reporting Phase or Post test Analysis phase
o    % Complete
o    % Defects Corrected
o    % Test Coverage
o    % Rework
o    % Test Cases Passed
o    % Test Effectiveness
o    % Test Cases Blocked
o    % Test Efficiency
o    1st Run Fail Rate
o    Defect Discovery Rate
o    Overall Fail Rate

III) Crucial Web Based Testing Metrics
Test Plan coverage on Functionality

Total number of requirement v/s number of requirements covered through test scripts.
•    (No of requirements covered / total number of requirements) * 100
Define requirements at the time of Effort estimation

Example: Total number of requirements estimated are 46, total number of requirements tested 39; blocked 7…define what is the coverage?

Note: Define requirement clearly at project level

Test Case defect density

Total number of errors found in test scripts v/s developed and executed.
•    (Defective Test Scripts  /Total Test Scripts) * 100

Example: Total test script developed 1360, total test script executed 1280, total test script passed 1065, total test script failed 215
So, test case defect density is
215 X 100
—————————- = 16.8%

This 16.8% value can also be called as test case efficiency %, which is depends upon total number of test cases which uncovered defects

Defect Slippage Ratio

Number of defects slipped (reported from production) v/s number of defects reported during execution.
•    Number of Defects Slipped / (Number of Defects Raised – Number of Defects Withdrawn)

Example: Customer filed defects are 21, total defect found while testing are 267, total number of invalid defects are 17

So, Slippage Ratio is
[21/ (267-17)] X 100 = 8.4%

Requirement Volatility

Number of requirements agreed v/s number of requirements changed.
•    (Number of Requirements Added + Deleted + Modified) *100 / Number of Original Requirements
•    Ensure that the requirements are normalized or defined properly while estimating
Example: VSS 1.3 release had total 67 requirements initially, later they added another 7 new requirements and removed 3 from initial requirements and modified 11 requirements

So, requirement Volatility is
(7 + 3 + 11) * 100/67 = 31.34%
Means almost 1/3 of the requirement changed after initial identification

Review Efficiency

The Review Efficiency is a metric that offers insight on the review quality and testing
Some organization also use this term as “Static Testing” efficiency and they are aiming to get min of 30% defects in static testing

Review efficiency=100*Total number of defects found by reviews/Total number of project defects

Example: A project found total 269 defects in different reviews, which were fixed and test team got 476 defects which were reported and valid

So, Review efficiency is [269/(269+476)] X 100 = 36.1%

Efficiency and Effectiveness of Processes

•    Effectiveness: Doing the right thing. It deals with meeting the desirable attributes that are expected by the customer.

•    Efficiency: Doing the thing right. It concerns the resources used for the service to be rendered

Metrics for Software Testing

•    Defect Removal Effectiveness

DRE= Defects removed during development phase   x100%
Defects latent in the product

Defects latent in the product = Defects removed during development
Phase+ defects found later by user

•    Efficiency of Testing Process (define size in KLoC or FP, Req.)

Testing Efficiency=  Size of Software Tested
Resources used

What is metric?
A measurement scale and the method used for measurement

What is measurement?
The process of assigning a number or category to an entity to describe an attribute of that entity.

What is measurement scale?
A scale that constrains the type of data analysis that can be performed on it.

What is measure?
The number or category assigned to an attribute of an entity by making a measurement.


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