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Using genetic algorithms and coupling measures to devise optimal integration test orders
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Source SEKE; Vol. 27 archive
Proceedings of the 14th international conference on Software engineering and knowledge engineering table of contents
Ischia, Italy
SESSION: Artificial intelligence approaches to software engineering table of contents
Pages: 43 - 50  
Year of Publication: 2002
ISBN:1-58113-556-4
Authors
Lionel C. Briand  Carleton University, Ottawa, ON, Canada
Jie Feng  Carleton University, Ottawa, ON, Canada
Yvan Labiche  Carleton University, Ottawa, ON, Canada
Publisher
ACM  New York, NY, USA
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ABSTRACT

We present here an improved strategy to devise optimal integration test orders in object-oriented systems. Our goal is to minimize the complexity of stubbing during integration testing as this has been shown to be a major source of expenditure. Our strategy to do so is based on the combined use of inter-class coupling measurement and genetic algorithms. The former is used to assess the complexity of stubs and the latter is used to minimize complex cost functions based on coupling measurement. Using a precisely defined procedure, we investigate this approach in a case study involving a real system. Results are very encouraging as the approach clearly helps obtaining systematic and optimal results.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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L. Briand, J. Feng and Y. Labiche, "Experimenting with Genetic Algorithms to Devise Optimal Integration Test Orders," Carleton University, Technical Report SCE-02-03, March, 2002, http://www.sce.carleton.ca/Squall/Articles/TR_SCE-02-03.pdf.
 
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CITED BY  9

Collaborative Colleagues:
Lionel C. Briand: colleagues
Jie Feng: colleagues
Yvan Labiche: colleagues