White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

New Strategies For Automated Random Testing

Ahmad, Mian Asbat (2014) New Strategies For Automated Random Testing. PhD thesis, University of York.

[img]
Preview
Text
ociamthesismain.pdf
Available under License Creative Commons Attribution-Noncommercial-No Derivative Works 2.0 UK: England & Wales.

Download (3902Kb) | Preview

Abstract

The ever increasing reliance on software-intensive systems is driving research to discover software faults more effectively and more efficiently. Despite intensive research, very few approaches have studied and used knowledge about fault domains to improve the testing or the feedback given to developers. The present thesis addresses this shortcoming: it leverages fault co-localization in a new random testing strategy called Dirt Spot Sweep- ing Random (DSSR), and it presents two new strategies: Automated Discovery of Failure Domain (ADFD) and Automated Discovery of Failure Domain+ (ADFD+). These improve the feedback given to developers by deducing more information about the failure domain (i.e. point, block, strip) in an automated way. The DSSR strategy adds the value causing the failure and its neighbouring values to the list of interesting values for exploring the underlying failure domain. The comparative evaluation showed significantly better performance of DSSR over Random and Random+ strategies. The ADFD strategy finds failures and failure domains and presents the pass and fail domains in graphical form. The results obtained by evaluating error-seeded numerical programs indicated highly effective performance of the ADFD strategy. The ADFD+ strategy is an extended version of ADFD strategy with respect to algorithm and graphical presentation of failure domains. In comparison with Randoop, ADFD+ strategy successfully detected all failures and failure domains while Randoop identified individual failures but could not detect failure domains. The ADFD and ADFD+ techniques were enhanced by integration of the automatic invariant detector Daikon, and the precision of identifying failure domains was determined through extensive experimental evaluation of real world Java projects contained in a database, namely Qualitas Corpus. The analyses of results, cross-checked by manual testing indicated that the ADFD and ADFD+ techniques are highly effective in providing assistance but are not an alternative to manual testing.

Item Type: Thesis (PhD)
Academic Units: The University of York > Computer Science (York)
Identification Number/EthosID: uk.bl.ethos.635418
Depositing User: Mr Mian Asbat Ahmad
Date Deposited: 17 Feb 2015 15:12
Last Modified: 08 Sep 2016 13:32
URI: http://etheses.whiterose.ac.uk/id/eprint/7981

You do not need to contact us to get a copy of this thesis. Please use the 'Download' link(s) above to get a copy.
You can contact us about this thesis. If you need to make a general enquiry, please see the Contact us page.

Actions (repository staff only: login required)