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Search-based prediction of fault-slip-through in large software projects
Responsible organisation
2010 (English)Conference paper, (Refereed) Published
Abstract [en]

A large percentage of the cost of rework can be avoided by finding more faults earlier in a software testing process. Therefore, determination of which software testing phases to focus improvements work on, has considerable industrial interest. This paper evaluates the use of five different techniques, namely particle swarm optimization based artificial neural networks (PSO-ANN), artificial immune recognition systems (AIRS), gene expression programming (GEP), genetic programming (GP) and multiple regression (MR), for predicting the number of faults slipping through unit, function, integration and system testing phases. The objective is to quantify improvement potential in different testing phases by striving towards finding the right faults in the right phase. We have conducted an empirical study of two large projects from a telecommunication company developing mobile platforms and wireless semiconductors. The results are compared using simple residuals, goodness of fit and absolute relative error measures. They indicate that the four search-based techniques (PSO-ANN, AIRS, GEP, GP) perform better than multiple regression for predicting the fault-slip-through for each of the four testing phases. At the unit and function testing phases, AIRS and PSO-ANN performed better while GP performed better at integration and system testing phases. The study concludes that a variety of search-based techniques are applicable for predicting the improvement potential in different testing phases with GP showing more consistent performance across two of the four test phases.

Place, publisher, year, edition, pages
Benevento: IEEE , 2010.
Keyword [en]
Faults-slip-through, Prediction, Search-based
National Category
Software Engineering Computer Science
Identifiers
URN: urn:nbn:se:bth-7806Local ID: oai:bth.se:forskinfoFEF4C2B39D13931EC1257740007EEF0DOAI: oai:DiVA.org:bth-7806DiVA: diva2:835468
Conference
2nd International Symposium on Search Based Software Engineering
Available from: 2012-09-18 Created: 2010-06-13 Last updated: 2015-06-30Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf