An Approach to estimate Software Testing
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This document provides an overview of the qEstimation analysis method for estimating the size and effort of software testing. It discusses estimating test size using Test Case Points (TCP), which represents the size of the simplest test case. TCP is calculated based on the number of checkpoints in a test case, and the complexity of the test setup and data. Estimated testing effort is then determined using the total TCP size and either a measured test velocity or a regression analysis of previous test cycles. The method aims to introduce a standardized way to estimate testing size and effort.
![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
– vague definitions of testing productivity
• due to the lack of a size measure for software testing
• 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](https://image.slidesharecdn.com/qestimation-121126230643-phpapp01/85/An-Approach-to-estimate-Software-Testing-4-320.jpg)
![qEstimation Analysis’ 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](https://image.slidesharecdn.com/qestimation-121126230643-phpapp01/85/An-Approach-to-estimate-Software-Testing-7-320.jpg)
![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
• [4] V. Lam, “Estimable”, Professional Tester,
http://www.professionaltester.com/magazine/backissue/PT015/ProfessionalTester-
July2012-Lam.pdf
• [5] QASymphony, “Test Case Point Analysis”, White Paper and Tool
http://www.qasymphony.com/white-papers.html](https://image.slidesharecdn.com/qestimation-121126230643-phpapp01/85/An-Approach-to-estimate-Software-Testing-19-320.jpg)
An Approach to estimate Software Testing
- 2. Agenda • Background and Motivation • qEstimation Analysis – Test Size Estimation (Test Case Point Analysis) – Test Effort Estimation • 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 • Methods and Metrics – Source Lines of Code (SLOC) – Function Points – Use Case Points – Story Points – COCOMO – Expert Judgment 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 – vague definitions of testing productivity • due to the lack of a size measure for software testing • 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 • Conclusion 5
- 6. qEstimation Analysis’ 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 Analysis’ 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 • Each type of test case is assigned a weight • Adjusted TCP of the test case = Counted TCP x Weight(*) (*) based on our survey of 18 senior QA engineers. You can adjust according to your project’s experience. 12
- 13. Estimate Effort of Test Cycle • Overview – Two estimation methods • Based on Test Velocity • 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 13
- 14. 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 • dependent on project • calculated based on data from completed test cycles of the same project 14
- 15. 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 = 0.0729x + 1.6408 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 15
- 16. Calibrate the qEstimation Estimation Model • Calibration: a process adjusting parameters for a model using historical data or experiences • With qEstimation, 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 (4) Effort distribution (5) Weights of test case types • Process can be done with the help of tools Tool Demo 16
- 17. Conclusion • qEstimation Analysis is an agile approach to estimating size and effort of test cycle – Estimate Size in TCP – 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 – Found useful for estimating testing effort • Limitations and future improvements – A new approach – Need more empirical validations 17
- 18. Thank You
- 19. 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 • [4] V. Lam, “Estimable”, Professional Tester, http://www.professionaltester.com/magazine/backissue/PT015/ProfessionalTester- July2012-Lam.pdf • [5] QASymphony, “Test Case Point Analysis”, White Paper and Tool http://www.qasymphony.com/white-papers.html