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...

Full description

Saved in:
Bibliographic Details
Published inAutomated Software Engineering: Proceedings of the 17th IEEE international conference on Automated software engineering; 23-27 Sept. 2002 pp. 237 - 240
Main Authors Owen, D., Menzies, T., Cukic, B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2002
Subjects
Automata
Computer science
Costs
Design for experiments
Machine learning
Mechanical factors
Search engines
Software engineering
Software testing
Stochastic processes
Online AccessGet full text

Cover

More Information
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.
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