What makes finite-state models more (or less) testable?
This paper studies how details of a particular model can effect the efficacy of a search for detects. We find that if the test method is fixed, we can identity classes of software that are more or less testable. Using a combination of model mutators and machine learning, we find that we can isolate...
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Published in | Automated Software Engineering: Proceedings of the 17th IEEE international conference on Automated software engineering; 23-27 Sept. 2002 pp. 237 - 240 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
2002
|
Subjects | |
Online Access | Get full text |
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Summary: | This paper studies how details of a particular model can effect the efficacy of a search for detects. We find that if the test method is fixed, we can identity classes of software that are more or less testable. Using a combination of model mutators and machine learning, we find that we can isolate topological features that significantly change the effectiveness of a defect detection tool. More specifically, we show that for one defect detection tool (a stochastic search engine) applied to a certain representation (finite state machines), we can increase the average odds of finding a defect from 69% to 91%. The method used to change those odds is quite general and should apply to other defect detection tools being applied to other representations. |
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Bibliography: | SourceType-Conference Papers & Proceedings-1 ObjectType-Conference Paper-1 content type line 25 |
ISBN: | 9780769517360 0769517366 |
ISSN: | 1938-4300 2643-1572 |
DOI: | 10.1109/ASE.2002.1115019 |