Cart (Loading....) | Create Account
Close category search window
 

Establishing a defect prediction model using a combination of product metrics as predictors via Six Sigma methodology

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Suffian, M.D.M. ; Test Centre of Excellence (Test COE), MIMOS Berhad, Kuala Lumpur, Malaysia ; Abdullah, M.R.

Defect prediction is an important aspect of the Product Development Life Cycle. The rationale in knowing predicted number of functional defects earlier on in the lifecycle, rather than to just find as many defects as possible during testing phase is to determine when to stop testing and ensure all the in-phase defects have been found in-phase before a product is delivered to the intended end user. It also ensures that wider test coverage is put in place to discover the predicted defects. This research is aimed to achieve zero known post release defects of the software delivered to the end user by MIMOS Berhad. To achieve the target, the research effort focuses on establishing a test defect prediction model using Design for Six Sigma methodology in a controlled environment where all the factors contributing to the defects of the product is within MIMOS Berhad. It identifies the requirements for the prediction model and how the model can benefit them. It also outlines the possible predictors associated with defect discovery in the testing phase. Analysis of the repeatability and capability of test engineers in finding defects are demonstrated. This research also describes the process of identifying characteristics of data that need to be collected and how to obtain them. Relationship of customer needs with the technical requirements of the proposed model is then clearly analyzed and explained. Finally, the proposed test defect prediction model is demonstrated via multiple regression analysis. This is achieved by incorporating testing metrics and development-related metrics as the predictors. The achievement of the whole research effort is described at the end of this study together with challenges faced and recommendation for future research work.

Published in:

Information Technology (ITSim), 2010 International Symposium in  (Volume:3 )

Date of Conference:

15-17 June 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.