qEstimation:An Approach for Estimating

Software Testing Effort using Test Case Point Analysis

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Agenda

• Background and Motivation• qEstimation Analysis

– Test Size Estimation (Test Case Point Analysis)– Test Effort Estimation

• qEstimation in Practice• Conclusion

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)

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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

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Agenda

• Background and Motivation• qEstimation Analysis

– Test Size Estimation (Test Case Point Analysis)– Test Effort Estimation

• qEstimation in Practice• Conclusion

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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)

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Test Case

qEstimation Process

Test CaseCount TCPs of all Test

Cases

Estimate Testing Effort

Parameters

Counted Size

Estimated Effort

[Test Cycle i]

Test Cycle Size Actual Effort

Effort by Activity

…. …. …. ….

Test cycle i …. …. ….

…. …. …. ….

Historical Data of this Project

Update Historical

Data

Calibrate Estimation

Model

Estimate size and effort of different test cycles of a same project:

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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:

Test Case

Count Checkpoints

Determine Set Up Complexity

Determine Test Data Complexity

Adjust based on Test Type(optional)

Unadjusted TCPs

TCPs

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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

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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 of TCP(*)

Complexity Level

Description

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.

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 Level

Description

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.

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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

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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.

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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• Test Execution• Test Tracking and Reporting

Each of these activities may be performed multiple times

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.

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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

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0 100 200 300 400 500 600 700 800 900 1000

0

10

20

30

40

50

60

70

80

90

100

f(x) = 0.0728501072514328 x + 1.64076780728634

Adjusted TCP

Eff

ort

(P

M)

Equation of Size and Effort

The data analysis tool like Excel can be used to find out the equation

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

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Tool Demo

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Agenda

• Background and Motivation• qEstimation Analysis

– Test Size Estimation (Test Case Point Analysis)– Test Effort Estimation

• qEstimation in Practice• Conclusion

Note #1: Complexity Categorization

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

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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

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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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Note #4: Historical Data

• Historical data is important– Effort spent on new release v.s maintenance release– Formalize timesheet reporting mechanisms

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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 ….

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1. Export effort from OrangeHRM using TimesheetReport tool2. Run VBScript to cut effort into Testing Phases (TP, TAD, TE, DTR)

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

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Agenda

• Background and Motivation• qEstimation Analysis

– Test Size Estimation (Test Case Point Analysis)– Test Effort Estimation

• qEstimation in Practice• Conclusion

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

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Test Case

qEstimation Process

Test CaseCount TCPs of all Test

Cases

Estimate Testing Effort

Parameters

Counted Size

Estimated Effort

[Test Cycle i]

Test Cycle Size Actual Effort

Effort by Activity

…. …. …. ….

Test cycle i …. …. ….

…. …. …. ….

Historical Data of this Project

Update Historical

Data

Calibrate Estimation

Model

Thank You

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