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Test Case Point Analysis

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This document discusses test case point analysis (TCPA) for estimating software testing projects. It introduces TCPA, which estimates testing size using test cases. The complexity of test cases is measured by counting checkpoints, preconditions, and test data. Test case points are adjusted based on test type. Estimated effort is then calculated using test case points and productivity metrics. The goal is to provide accurate estimates of testing size, effort, schedule and staff specifically for testing projects.

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Test Case Point Analysis
Agenda • Objectives • Background and Motivation • Existing Estimation Methods • Test Case Point Analysis • Effort Estimation Methods using TCP • Conclusion and Future Work 2
Objectives • Discuss importance of software estimation, especially for testing projects • Discuss existing methods for estimating testing activities • Introduce an approach to estimating testing projects – Test Case Point Analysis – Effort Estimation using TCP 3
Agenda • Objectives • Background and Motivation • Existing Estimation Methods • Test Case Point Analysis • Effort Estimation Methods using TCP • Conclusion and Future Work 4
Software Estimation and Its Importance • 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 • Provides inputs for – Making investment decisions – Budget and staff allocation – Tradeoff and risk analysis – Project planning – Stakeholder negotiation – etc. 5
Popular Software Estimation Methods • Sizing Methods – Source Lines of Code (SLOC) – Function Points Analysis – Use Case Points • Effort Estimation Methods – Expert judgment/experience – Productivity index – COCOMO, SEER-SIM, SLIM models • Software testing estimation methods – Using a test distribution percentage – Test Case Points Analysis (by Cognizant Technology Solutions) [2] • Mainly based on the number of steps 6
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 • There are needs of – accurately estimating effort of testing activities – measuring size and productivity of testing activities – measuring effectiveness and efficiency of software testing 7
Motivation (cont’d) • Our aim to – Defining a method for estimating the size of testing activities – Defining methods to estimate testing effort, schedule, and staff accurately using this size measure – Using this size measure as a basis for measuring • productivity • effectiveness • other testing metrics 8
Agenda • Objectives • Background and Motivation • Existing Estimation Methods • Test Case Point Analysis • Effort Estimation Methods using TCP • Conclusion and Future Work 9
Test Case Point Analysis • Principles – Size must reflect the mass and complexity of the testing project – Size should correlate with testing effort – Test case point is measured using test cases as main input – Test case complexity is based on • Checkpoints • Precondition • Test Data • Type of test case 10
Test Case Point Analysis (cont’d) • Process overview Project Attributes Test Case Estimate Estimated Test Case Test Case Points Testing Effort Point Analysis Effort 11
Test Case Point Analysis (cont’d) • TCPA estimates the size of testing projects using test cases as input • Steps 1. Measure test case complexity • Count checkpoints • Measure the complexity of precondition • Measure the complexity of data 2. Adjust Test Case Point by type of test • Consider 11 types of test case o Each type has a weight o Adjust TCP using the weight • Use functional test cases as baseline with the weight of 1.0 12
Checkpoint • 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 • Counting rule 1 One checkpoint is counted as one Test Case Point 13
Test Case Precondition • Precondition – specifies the condition to execute the test case – mainly affects the cost to execute the test case – may be related to data prepared for the test case • Four levels of complexity – None – Low – Medium – High 14
Test Case Precondition (cont’d) • None – The precondition is not applicable or important to execute the test case – Or, the precondition is just reused from the previous test case to continue the current test case • Low – The condition for executing the test case is available with some simple modifications required – Or, some simple set-up steps are needed • Medium – Some explicit preparation is needed to execute the test case – The condition for executing is available with some additional modifications required – Or, some additional set-up steps are needed • High – Heavy hardware and/or software configurations are needed to execute the test case 15
Test Case Precondition (cont’d) • Counting rule 2A: Counting Unadjusted Test Case Points for Precondition: Each complexity level of precondition is assigned a number of Test Case Points Based on our survey of 18 senior QA engineers 16
Test Data • Test Data – used to execute the test case – 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, or for the whole system • Four levels of complexity – None – Low – Medium – High 17
Test Data (cont’d) • None – No test data preparation is needed • 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 • Medium – Test data is deliberately prepared in advance with extra effort to ensure its completeness, comprehensiveness, and consistency • 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 18
Test Data (cont’d) • Counting rule 2B: Counting Unadjusted Test Case Points for Test Data: Each complexity level of Test Data is assigned a number of Test Case Points Based on our survey of 18 senior QA engineers 19
Adjust Test Case Point • Test Case Point counted till this point is considered Unadjusted Test Case Point (UTCP) • 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 20
Weight by Type of Test Based on our survey of 18 senior QA engineers 21
Weight by Type of Test (cont’d) 22
Test Case Point Analysis - Summary • Process overview Count Checkpoints Determine Test Case UTCP Adjust with TCP Precondition Test Type Complexity Determine Test Data Complexity 23
Agenda • Objectives • Background and Motivation • Existing Estimation Methods • Test Case Point Analysis • Effort Estimation Methods using TCP • Conclusion and Future Work 24
Estimate Testing Effort • Estimate testing effort using TCP • Test effort distribution, four phases – Test Planning – Test Analysis and Design Each of these phases may be performed multiple times – Test Execution – Test Tracking and Reporting • Effort estimation methods – Productivity index – Regression 25
Estimate Testing Effort (cont’d) • When estimating TCP, assume that each phase is performed ONCE • Effort is estimated dependent on how many times a phase is performed 26
Productivity Index • Effort is computed using productivity index of completed project • Productivity index is measured as person-month per TCP Effort = TCP * Productivity Index Simple method 27
Regression • Estimate effort of new projects using size and effort of completed projects 100 90 Regression 80 formula 70 y = 0.072x + 1.640 Effort (PM) 60 50 40 30 20 10 0 0 100 200 300 400 500 600 700 800 900 1000 Adjusted TCP 28
Tools Support by QASymphony [3] • qTest – Quality Management Suite – Manage requirements and test cases – Plan testing activities and resources – Monitor and control testing activities – Perform measurement, analysis, and reporting • Test case point analysis • Effort estimation • qTrace – defect and scenario recording tool – Record defect – Record user scenarios 29
Conclusion and Future Work • It is important to estimate size and effort of testing projects • Test Case Point Analysis (TCPA) was proposed to estimate size. Elements include – Checkpoints – Precondition – Test Data – Test Type • Simple two methods for estimating effort were introduced – Productivity index – Regression 30
Conclusion and Future Work • Advantages of TCPA – Easy to implement – Reflecting real complexity of test cases – Independent with the number of steps • Limitations and future work – Need empirical validations – Recalibrate constants used in the method – Support only manual testing – Lack of support for performance testing 31
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

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Test Case Point Analysis

  • 2. Agenda • Objectives • Background and Motivation • Existing Estimation Methods • Test Case Point Analysis • Effort Estimation Methods using TCP • Conclusion and Future Work 2
  • 3. Objectives • Discuss importance of software estimation, especially for testing projects • Discuss existing methods for estimating testing activities • Introduce an approach to estimating testing projects – Test Case Point Analysis – Effort Estimation using TCP 3
  • 4. Agenda • Objectives • Background and Motivation • Existing Estimation Methods • Test Case Point Analysis • Effort Estimation Methods using TCP • Conclusion and Future Work 4
  • 5. Software Estimation and Its Importance • 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 • Provides inputs for – Making investment decisions – Budget and staff allocation – Tradeoff and risk analysis – Project planning – Stakeholder negotiation – etc. 5
  • 6. Popular Software Estimation Methods • Sizing Methods – Source Lines of Code (SLOC) – Function Points Analysis – Use Case Points • Effort Estimation Methods – Expert judgment/experience – Productivity index – COCOMO, SEER-SIM, SLIM models • Software testing estimation methods – Using a test distribution percentage – Test Case Points Analysis (by Cognizant Technology Solutions) [2] • Mainly based on the number of steps 6
  • 7. 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 • There are needs of – accurately estimating effort of testing activities – measuring size and productivity of testing activities – measuring effectiveness and efficiency of software testing 7
  • 8. Motivation (cont’d) • Our aim to – Defining a method for estimating the size of testing activities – Defining methods to estimate testing effort, schedule, and staff accurately using this size measure – Using this size measure as a basis for measuring • productivity • effectiveness • other testing metrics 8
  • 9. Agenda • Objectives • Background and Motivation • Existing Estimation Methods • Test Case Point Analysis • Effort Estimation Methods using TCP • Conclusion and Future Work 9
  • 10. Test Case Point Analysis • Principles – Size must reflect the mass and complexity of the testing project – Size should correlate with testing effort – Test case point is measured using test cases as main input – Test case complexity is based on • Checkpoints • Precondition • Test Data • Type of test case 10
  • 11. Test Case Point Analysis (cont’d) • Process overview Project Attributes Test Case Estimate Estimated Test Case Test Case Points Testing Effort Point Analysis Effort 11
  • 12. Test Case Point Analysis (cont’d) • TCPA estimates the size of testing projects using test cases as input • Steps 1. Measure test case complexity • Count checkpoints • Measure the complexity of precondition • Measure the complexity of data 2. Adjust Test Case Point by type of test • Consider 11 types of test case o Each type has a weight o Adjust TCP using the weight • Use functional test cases as baseline with the weight of 1.0 12
  • 13. Checkpoint • 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 • Counting rule 1 One checkpoint is counted as one Test Case Point 13
  • 14. Test Case Precondition • Precondition – specifies the condition to execute the test case – mainly affects the cost to execute the test case – may be related to data prepared for the test case • Four levels of complexity – None – Low – Medium – High 14
  • 15. Test Case Precondition (cont’d) • None – The precondition is not applicable or important to execute the test case – Or, the precondition is just reused from the previous test case to continue the current test case • Low – The condition for executing the test case is available with some simple modifications required – Or, some simple set-up steps are needed • Medium – Some explicit preparation is needed to execute the test case – The condition for executing is available with some additional modifications required – Or, some additional set-up steps are needed • High – Heavy hardware and/or software configurations are needed to execute the test case 15
  • 16. Test Case Precondition (cont’d) • Counting rule 2A: Counting Unadjusted Test Case Points for Precondition: Each complexity level of precondition is assigned a number of Test Case Points Based on our survey of 18 senior QA engineers 16
  • 17. Test Data • Test Data – used to execute the test case – 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, or for the whole system • Four levels of complexity – None – Low – Medium – High 17
  • 18. Test Data (cont’d) • None – No test data preparation is needed • 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 • Medium – Test data is deliberately prepared in advance with extra effort to ensure its completeness, comprehensiveness, and consistency • 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 18
  • 19. Test Data (cont’d) • Counting rule 2B: Counting Unadjusted Test Case Points for Test Data: Each complexity level of Test Data is assigned a number of Test Case Points Based on our survey of 18 senior QA engineers 19
  • 20. Adjust Test Case Point • Test Case Point counted till this point is considered Unadjusted Test Case Point (UTCP) • 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 20
  • 21. Weight by Type of Test Based on our survey of 18 senior QA engineers 21
  • 22. Weight by Type of Test (cont’d) 22
  • 23. Test Case Point Analysis - Summary • Process overview Count Checkpoints Determine Test Case UTCP Adjust with TCP Precondition Test Type Complexity Determine Test Data Complexity 23
  • 24. Agenda • Objectives • Background and Motivation • Existing Estimation Methods • Test Case Point Analysis • Effort Estimation Methods using TCP • Conclusion and Future Work 24
  • 25. Estimate Testing Effort • Estimate testing effort using TCP • Test effort distribution, four phases – Test Planning – Test Analysis and Design Each of these phases may be performed multiple times – Test Execution – Test Tracking and Reporting • Effort estimation methods – Productivity index – Regression 25
  • 26. Estimate Testing Effort (cont’d) • When estimating TCP, assume that each phase is performed ONCE • Effort is estimated dependent on how many times a phase is performed 26
  • 27. Productivity Index • Effort is computed using productivity index of completed project • Productivity index is measured as person-month per TCP Effort = TCP * Productivity Index Simple method 27
  • 28. Regression • Estimate effort of new projects using size and effort of completed projects 100 90 Regression 80 formula 70 y = 0.072x + 1.640 Effort (PM) 60 50 40 30 20 10 0 0 100 200 300 400 500 600 700 800 900 1000 Adjusted TCP 28
  • 29. Tools Support by QASymphony [3] • qTest – Quality Management Suite – Manage requirements and test cases – Plan testing activities and resources – Monitor and control testing activities – Perform measurement, analysis, and reporting • Test case point analysis • Effort estimation • qTrace – defect and scenario recording tool – Record defect – Record user scenarios 29
  • 30. Conclusion and Future Work • It is important to estimate size and effort of testing projects • Test Case Point Analysis (TCPA) was proposed to estimate size. Elements include – Checkpoints – Precondition – Test Data – Test Type • Simple two methods for estimating effort were introduced – Productivity index – Regression 30
  • 31. Conclusion and Future Work • Advantages of TCPA – Easy to implement – Reflecting real complexity of test cases – Independent with the number of steps • Limitations and future work – Need empirical validations – Recalibrate constants used in the method – Support only manual testing – Lack of support for performance testing 31
  • 33. 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