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Different Software Quality Metrics used by Expert Test Managers
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Different Software Quality Metrics used by Expert Test Managers

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Different Software Quality Metrics used by Expert Test Managers

Article by: Kushal Kar & Swastika Nandi - Guest Publishers of the article.

Software Metric is a generic name for the measure of Quality of the Software Product. Software Metric can be a reflection on the status under software development cycle or some results etc.

A good project manager is the one who applies the principles of metrics to plan, organize & control the project deliverables in quantifiable / measurable terms.

Some of the software metrics extensively used by ISTQB certified expert testing managers are described below.

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Description of Metric

How to Measure (Formulae)


Test Coverage

Number of units (KLOC/FP) tested / total size of the system


Quality of Testing

No. of defects found during Testing/(No. of defects found during testing + No of acceptance defects found after delivery) *100


Effort Variance

{(Actual Efforts-Estimated Efforts) / Estimated Efforts} *100


Schedule Variance

{(Actual Duration - Estimated Duration)/Estimated Duration} *100


Test Effectiveness


Explanation: Here "t" is the total number of defects reported during the testing, whereas UAT, means the total number of defects that are reported during the user acceptance testing.


Defect Density

No of Defects / Size (FP or KLOC)

Explanation: Here FP = Function Points


Weighted Defect Density

(5*Count of fatal defects)+(3*Count of Major defects)+(1*Count of minor defects)

Here 5,3,1 corresponds to the severity of the detect


Schedule Slippage

(Actual End date - Estimated End date) / (Planned End Date – Planned Start Date) * 100


Rework Effort Ratio

(Actual review effort spent in that particular phase / Total actual efforts spent in that phase) * 100


Requirement Stability Index

{1 - (Total number of changes /number of initial requirements)}


Requirement Creep

(Total Number of requirements added/Number of initial requirements) * 100



Defects / KLOC or

Defects / Function points



MTTC (Mean time to change) -- Once error is found, how much time it takes to fix it in production.



Integrity = Summation [(1 - threat) X (1 - security)]



User questionnaire survey results will give an indication of the same.

Comment: How easy it’s for the users to use the system and how fast they are able to learn operating the system.


CSAT – Customer Satisfaction Index

Call volume to customer service hotline Availability (percentage of time a system is available, versus the time the system is needed to be available)



# Mean time between failures (MTBF) - Total operating time divided by the number of failures. MTBF is the inverse of failure rate.

# Mean time to repair (MTTR) - Total elapsed time from initial failure to the reinitiating of system status. Mean Time To Restore includes Mean Time To Repair (MTBF + MTTR = 1.)

# Reliability ratio = (MTBF / MTTR)

Explanation: Reliability is the probability that an item will perform a required function under stated conditions for a stated period of time. The probability of survival, R(t), plus the probability of failure, F(t), is always unity. Expressed as a formula : F(t) + R(t) = 1 or, F(t)=1 - R(t).

# Mean time between failure (MTBF)

# Mean time to repair (MTTR)

# Reliability ratio (MTBF / MTTR)


Defect Ratios

# Defects found after product delivery per function point.

# Defects found after product delivery per LOC

# Pre-delivery defects: annual post-delivery defects

# Defects per function point of the system modifications


Number of Tests per Unit Size

Number of test cases per KLOC/FP


Acceptance Criteria Tested

Acceptance criteria tested / total acceptance criteria


Defects Per Size

Defects detected / system size


Testing Cost

Cost of testing / total cost *100


Cost to locate defect

Cost of testing / the number of defects located


Achieving Budget

Actual cost of testing / Budgeted cost of testing


Defects detected in testing

Defects detected in testing / total system defects


Defects detected in production

Defects detected in production/system size


Quality of Testing

No of defects found during Testing/(No of defects found during testing + No of acceptance defects found after delivery) *100


Effectiveness of testing to business

= Loss due to problems / total resources processed by the system


System complaints

Number of third party complaints / number of transactions processed


Scale of Ten

Assessment of testing by giving rating in scale of 1 to 10


Source Code Analysis

Number of source code statements changed / total number of tests


Test Planning Productivity

No of Test cases designed / Actual Effort for Design and Documentation


Test Execution Productivity

No of Test cycles executed / Actual Effort for testing

About the Authors: Kushal Kar & Swastika Nandi – QA Analysts, are the guest publishers of the article & are solely responsible for the ownership of its contents.

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