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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:635418 |
| Date | January 2014 |
| Creators | Ahmad, Mian Asbat |
| Publisher | University of York |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.whiterose.ac.uk/7981/ |
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