IDEAS home Printed from https://ideas.repec.org/h/spr/ssrchp/978-1-84800-113-8_12.html
   My bibliography  Save this book chapter

Software Fault Imputation in Noisy and Incomplete Measurement Data

In: Recent Advances in Reliability and Quality in Design

Author

Listed:
  • Andres Folleco

    (Florida Atlantic University)

  • Taghi M. Khoshgoftaar

    (Florida Atlantic University)

  • Jason Hulse

    (Florida Atlantic University)

Abstract

This study examines the impact of noise on the evaluation of software quality imputation techniques. The imputation procedures evaluated in this work include Bayesian multiple imputation, mean imputation, nearest neighbor imputation, regression imputation, and REPTree (decision tree) imputation. These techniques were used to impute missing software measurement data for a large military command, control, and communications system dataset (CCCS). A randomized three-way complete block design analysis of variance model using the average absolute error as the response variable was built to analyze the imputation results. Multiple pairwise comparisons using Fisher and Tukey-Kramer tests were conducted to demonstrate the performance differences amongst the significant experimental factors. The underlying quality of data was a significant factor affecting the accuracy of the imputation techniques. Bayesian multiple imputation and regression imputation were top performers, while mean imputation was ineffective.

Suggested Citation

  • Andres Folleco & Taghi M. Khoshgoftaar & Jason Hulse, 2008. "Software Fault Imputation in Noisy and Incomplete Measurement Data," Springer Series in Reliability Engineering, in: Hoang Pham (ed.), Recent Advances in Reliability and Quality in Design, chapter 12, pages 255-274, Springer.
    • Handle: RePEc:spr:ssrchp:978-1-84800-113-8_12
    DOI: 10.1007/978-1-84800-113-8_12
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ssrchp:978-1-84800-113-8_12. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.