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A Method for Predicting Defects in System Testing for V-Model
A Method for Predicting Defects in System Testing for V-Model
A Method for Predicting Defects in System Testing for V-Model
A Method for Predicting Defects in System Testing for V-Model
A Method for Predicting Defects in System Testing for V-Model
A Method for Predicting Defects in System Testing for V-Model
A Method for Predicting Defects in System Testing for V-Model
A Method for Predicting Defects in System Testing for V-Model
A Method for Predicting Defects in System Testing for V-Model
A Method for Predicting Defects in System Testing for V-Model
A Method for Predicting Defects in System Testing for V-Model
A Method for Predicting Defects in System Testing for V-Model
A Method for Predicting Defects in System Testing for V-Model
A Method for Predicting Defects in System Testing for V-Model
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A Method for Predicting Defects in System Testing for V-Model

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Paper presented during 2nd Postgraduate Annual Research in Informatics Seminar 2012 (PARIS2012)

Paper presented during 2nd Postgraduate Annual Research in Informatics Seminar 2012 (PARIS2012)

Published in: Education, Technology, Business
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  • 1. PARIS 2012 A Method for Predicting Defects in System Testing for V-Model (Paper ID: 37) Muhammad Dhiauddin bin Mohamed Suffian Faculty of Computer Science & Information System mdhiauddin2@live.utm.my AP Dr. Suhaimi bin Ibrahim Advanced Informatics School suhaimiibrahim@utm.my
  • 2. Presentation Outline • • • • • Introduction Related Work Findings and Discussion Case Study Conclusion and Recommendation
  • 3. Introduction • V-model emphasizes on early testing activities: – rigor verification and validation activities throughout the phases: reviews, inspection, unit testing, integration testing and system testing • System testing is important in V-model: – Carried out by independent team – Ensure that all defects are discovered within the phase – Validate that the software or system under test meets the specification • Independent testing team faces challenges in completing test – Meeting the deadline to ensure on-time release – Finding as many defects as possible Problem • There is a need to have early indicator of total defects to be found in system testing before it starts • Systematic method needs to be developed for predicting defects in system testing using metrics from prior phases (development-related + testing-related activities)
  • 4. Introduction (cont.) • How does prediction help independent testing team?
  • 5. Related Work
  • 6. Findings and Discussion
  • 7. Findings and Discussion(cont.)
  • 8. Findings and Discussion(cont.)
  • 9. Findings and Discussion(cont.)
  • 10. Case Study Data set for regression analysis Metrics • Number of requirement pages • Number of design pages • Code size in a form of lines of code • Total test cases • Total effort in test case design • Total effort in phases prior to system testing • Requirement error • Design error • Code error • Test cases error • Total defects logged in a form of all defects and functional defects
  • 11. Case Study (cont.)
  • 12. Case Study (cont.) Verification result Selected equation Functional Defects = 4.00 - 0.204 Requirement Error - 0.631 Coding Error + 1.90 KLOC – 0.140 Requirement Page + 0.125 Design Page – 0.169 Total Test Cases + 0.221Total Effort Days
  • 13. Conclusion and Recommendation • Achievement: – The proposed method provides systematic way towards predicting defects for system testing in V-model by using prior phases’ metrics. – Statistical analysis used serves as the powerful tool to measure how good the method is in determining the accuracy of the prediction – Having maximum and minimum range for predicting defects allows independent testing team to have a control plan on what to do should the prediction does not fall within the specified range • Future works: – More metrics are taken into consideration to construct the prediction, particularly product-related metrics. – Future prediction could also forecast non-functional defects as well as defects based on severity. – Having specific prediction for different types of software which makes it more practical and useful.
  • 14. THANK YOU

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