ISSN:
1573-0409
Keywords:
Rule-based systems
;
goodness values
;
software engineering
;
artificial intelligence
;
software testing
;
branch coverage
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Abstract Test data generation using traditional software testing methods generally requires considerable manual effort and generates only a limited number of test cases before the amount of time expanded becomes unacceptably large. A rule-based framework that will automatically generate test data to achieve maximal branch coverage is presented. The design and discovery of rules used to generate meaningful test cases are also described. The rule-based approach allows this framework to be extended to include additional testing requirements and test case generation knowledge.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00444293
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