| Using genetic algorithms and coupling measures to devise optimal integration test orders |
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SEKE; Vol. 27
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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
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Downloads (6 Weeks): 8, Downloads (12 Months): 57, Citation Count: 9
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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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[doi> 10.1145/337180.337197]
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CITED BY 9
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Mark Harman , Youssef Hassoun , Kiran Lakhotia , Phil McMinn , Joachim Wegener, The impact of input domain reduction on search-based test data generation, Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering, September 03-07, 2007, Dubrovnik, Croatia
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