The document introduces the qEstimation method for estimating the size and effort of software testing activities. It discusses estimating test size using Test Case Points (TCP) by analyzing checkpoints, test setup complexity, and test data complexity. Effort can then be estimated using test velocity/productivity or regression analysis of historical size and effort data. The method is implemented in a qEstimation toolkit for easily counting TCPs, calibrating estimates, and monitoring test metrics. The approach provides an agile way to estimate testing independent of test case details. More empirical validation is still needed but initial experiences have been positive.

2. Agenda
• Background and Motivation
• qEstimation Analysis
– Test Size Estimation (Test Case Point Analysis)
– Test Effort Estimation
• qEstimation in Practice
• Conclusion
2

3. Background
• Software estimation
– Process of determining the cost, time, staff, and other related
attributes of software projects, often before work is performed
• Estimation is important for the success or failure of software
projects
– Making investment decisions
– Project planning
– Stakeholder/Client negotiation
• Estimation Methods
– Source Lines of Code (SLOC)
– Function Points
– Use Case Points/Story Points
– Expert Judgment (Experience Based)
3

4. Motivation
• Testing accounts for up to 50% of project effort [1]
• Current problems
– estimates are done for the whole project rather than testing specific
– lack of reliable methods designed for estimating size and effort of
software testing
• Currently, there are needs of:
– accurately estimating effort of testing activities
• How many days is needed for test design, test execution, etc.?
– measuring size and productivity of testing activities
• Your QA team is working less productive!
– measuring effectiveness and efficiency of software testing
• Why testing last release left so many bugs to Production?
• Our aim
– To introduce a method for estimating the size of testing activities
– To discuss methods to estimate testing effort using this size measure
– To introduce a simple toolkit for this estimation process
4

5. Agenda
• Background and Motivation
• qEstimation Analysis
– Test Size Estimation (Test Case Point Analysis)
– Test Effort Estimation
• qEstimation in Practice
• Conclusion
5

6. qEstimation Principles
• Size reflects the mass and complexity of each test cycle
of a testing project
• Test case’s complexity is based on
– Number of checkpoints
– Complexity of test setup or precondition
– Complexity of test data
• Test Case Point (TCP) is used as size unit
– representing the size of the most simple test case
• Calibration or model refinement is key to estimating
effort
– calibration based on feedback from different cycles within
project or of similar projects
• Focusing on independent testing (V & V)
6

7. qEstimation Process
Estimate size and effort of different test cycles of a same project:
[Test Cycle i]
Count TCPs Estimate
Test Case Counted Estimated
of all Test Testing
Test Case Size
Effort
Effort
Cases
Update
Parameters Historical
Data
Calibrate
Estimation Historical Data of this Project
Model
Test Cycle Size Actual Effort by
Effort Activity
…. …. …. ….
Test cycle i …. …. ….
…. …. …. ….
7

8. Count Size of Test Cycle
• Size of a test cycle is the total of TCPs of all test cases to be
executed in that test cycle
• Steps:
Count
Checkpoints
Adjust based on
Test Case Determine Set Up Unadjusted
Test Type TCPs
Complexity TCPs
(optional)
Determine Test
Data Complexity
8

9. Count Size of Test Cycle (cont’d)
• Checkpoints
– Checkpoint is the condition in which the tester verifies
whether the result produced by the target function
matches the expected criterion
– One test case consists of one or many checkpoints
One checkpoint is counted as one TCP
9

10. Count Size of Test Cycle (cont’d)
• Test Setup or Precondition
– Test setup specifies the condition to execute the test case
• Include setup steps to prepare environment for testing
• Mainly affect the cost to execute the test case
• May be related to data prepared for the test case
– Four levels of Test Setup complexity
• Each is assigned a number of TCPs
Number Complexity Description
of TCP(*) Level
0 None • The set up is not applicable or important to execute the test case
• Or, the set up is just reused from the previous test case to continue the current test case
1 Low • The condition for executing the test case is available with some simple modifications required
• Or, some simple set-up steps are needed
3 Medium • Some explicit preparation is needed to execute the test case
• Or, The condition for executing is available with some additional modifications required
• Or, some additional set-up steps are needed
5 High • Heavy hardware and/or software configurations are needed to execute the test case
(*) based on our survey of 18 senior QA engineers. You can adjust according to your project’s experience.
10

11. Count Size of Test Cycle (cont’d)
• Test Data
– Test Data is used to execute the test case
• It can be generated at the test case execution time, sourced from previous tests, or
generated by test scripts
• Test data is test case specific, or general to a group of test cases
– Four levels of Test Data complexity
• Each is assigned a number of TCPs
Number of TCP Complexity Description
(*) Level
0 None • No test data preparation is needed
1 Low • Simple test data is needed and can be created during the test case execution time
• Or, the test case uses a slightly modified version of existing test data and requires
little or no effort to modify the test data
3 Medium • Test data is deliberately prepared in advance with extra effort to ensure its
completeness, comprehensiveness, and consistency
6 High • Test data is prepared in advance with considerable effort to ensure its completeness,
comprehensiveness, and consistency
• This could include using support tools to generate data and a database to store and
manage test data
• Scripts may be required to generate test data
(*) based on our survey of 18 senior QA engineers. You can adjust according to your project’s experience.

12. Count Size of Test Cycle (cont’d)
• Adjust TCPs based on Type of Test
– This is an OPTIONAL step
– Adjustment is based on types of test cases
• Test Case Point counted till this point is considered Unadjusted
Test Case Point (UTCP)
n
UTCP = ∑TCPi
i=1
• UTCP is adjusted by considering types of test case
– Each type of test case is assigned a weight
– Adjusted Test Case Point (ATCP):
n
ATCP = ∑UTCPi * Wi
i=1
• UTCPi - the number of UTCP counted for the test case ith.
• Wi - the weight of the test case ith, taking into account its test type
12

13. Count Size of Test Cycle (cont’d)
• Type of Test
(*) based on our survey
of 18 senior QA
engineers. You can
adjust according to
your project’s
experience.
13

14. Estimate Effort of Test Cycle
• Overview
– Two estimation methods
• Based on Test Velocity/Productivity
• Regression Analysis of Size and Effort of completed test cycles
– Effort distributed by activity
• Test Planning
• Test Analysis and Design Each of these activities may
be performed multiple times
• Test Execution
• Test Tracking and Reporting
14

15. Estimate Effort of Test Cycle (cont’d)
• Estimate Effort based on Test Velocity
Effort(person-hour) = Size(TCP) / Test Velocity(TCP per person-hour)
– Test Velocity is measured as TCP/person-hour
• Test Velocity is dependent on project
• It should be calculated based on data from completed test cycles of the
similar projects.
15

16. Estimate Effort of Test Cycle (cont’d)
• Estimate effort using Linear Regression Analysis
– Find out the equation of effort and size using similar completed test
cycles of a project
100
90
80 Equation of
70 Size and Effort
y = 0.072x + 1.640
Effort (PM)
60
50
40 The data analysis tool like
30 Excel can be used to find
20
out the equation
10
0
0 100 200 300 400 500 600 700 800 900 1000
Adjusted TCP
16

17. Calibrate the qEstimation Model
• Calibration: a process adjusting parameters for a
model using historical data or experiences
• With qEstimation tool, you can calibrate:
(1) TCP assigned to each complexity level of Test Setup
(2) TCP assigned to each complexity level of Test Data
(3) Test Velocity/Productivity
(4) Effort distribution
(5) Weights of test case types
• Process can be done with the help of tools
Tool Demo
17

18. Agenda
• Background and Motivation
• qEstimation Analysis
– Test Size Estimation (Test Case Point Analysis)
– Test Effort Estimation
• qEstimation in Practice
• Conclusion
18

19. Note #1: Complexity Categorization
• Complexity Categorization: of Test Data and
Precondition
– Select “Medium” (Remember bell
curve in probability theory)
19

20. Note #2: Fast Test Design
• Lack time for detailing test cases while estimation
 Use fast test design techniques
– Requirement  Test scenario  High level test case
– Select Medium for unknown Test Data and Precondition.
Again, refer to bell curve chart
20

21. Note #3: Development v.s Maintenance
• Development v.s Maintenance release  Trace TCP
count for new feature release and maintenance
release project separately
– New feature: requirement analysis, test case
development, lot of bugs…
– Maintenance: Effort mostly is bug validation and
regression test execution, little effort is for test
development and bug reporting
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22. Note #4: Historical Data
• Historical data is important
– Effort spent on new release v.s maintenance release
– Formalize timesheet reporting mechanisms
22

23. Note #5: Tracking Actual Effort
Convention:
[Client] – [Project] – [Testing] – [Project Name – Development/Maintenance – Phase
of Testing – Any text describe the activity]
• Development: Project develops new features, major changes in application …
• Maintenance: Project that mainly fixes bugs, develops small features ….
1. Export effort from OrangeHRM using TimesheetReport tool
2. Run VBScript to cut effort into Testing Phases (TP, TAD, TE, DTR)
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24. Note #6: Monitoring Testing Metrics
We have the size in hand, we can easily measure below metrics
and use it as quality monitoring indicators:
• Productivity:
– Definition: How productive the team is
– Formula: TCPs/Actual Effort (in Hrs)
• Defect Rate/Defect Density:
– Definition: Number of bugs per an unit of work (here is TCP)
– Formula: No of bugs/Total TCPs
• Defect Escape Rate:
– Definition: The bugs escape to next round of testing or to Production
environment
– (Traditional view) Formula: No of Escaped Bugs / Total Bugs
– (Different view) Formula: No of Escaped Bugs / Total TCPs
24

25. Agenda
• Background and Motivation
• qEstimation Analysis
– Test Size Estimation (Test Case Point Analysis)
– Test Effort Estimation
• qEstimation in Practice
• Conclusion
25

26. Conclusion
• qEstimation tool is an agile approach to estimating size
and effort of test cycle
– Estimate Size in TCP by measuring the complexity of test cases
– Estimate Effort using Test Velocity or Regression
– An Excel toolkit to simplify the approach
• Advantages and experiences learned
– Easy to implement
– Reflecting real complexity of test cases
– Independent with the level of details of test cases
– Useful for estimating testing effort
• Limitations and future improvements
– A new approach – introduced and applied almost a year now
– Need more empirical validations
26

27. qEstimation Process
[Test Cycle i]
Count TCPs Estimate
Test Case Counted Estimated
of all Test Testing
Test Case Size
Effort
Effort
Cases
Update
Parameters Historical
Data
Calibrate
Estimation Historical Data of this Project
Model
Test Cycle Size Actual Effort by
Effort Activity
…. …. …. ….
Test cycle i …. …. ….
…. …. …. ….
27

28. Thank You

29. References
• [1] Y. Yang, Q. Li, M. Li, Q. Wang, An empirical analysis on distribution patterns of software
maintenance effort, International Conference on Software Maintenance, 2008, pp. 456-459
• [2] N. Patel, M. Govindrajan, S. Maharana, S. Ramdas, “Test Case Point Analysis”, Cognizant
Technology Solutions, White Paper, 2001
• [3] QASymphony: www.qasymphony.com