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Paris 2012: Predicting Defects in System Testing for V-Model (Paper ID: 37
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
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)
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
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.