Data Diversity: An Approach to Software Fault Tolerance
IEEE Transactions on Computers - Fault-Tolerant Computing
Investigations of the software testing coupling effect
ACM Transactions on Software Engineering and Methodology (TOSEM)
An Exploration of Software Faults and Failure Behaviour in a Large Population of Programs
ISSRE '04 Proceedings of the 15th International Symposium on Software Reliability Engineering
MuJava: an automated class mutation system: Research Articles
Software Testing, Verification & Reliability
An empirical analysis and comparison of random testing techniques
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Adaptive Random Testing with Enlarged Input Domain
QSIC '06 Proceedings of the Sixth International Conference on Quality Software
A Domain Strategy for Computer Program Testing
IEEE Transactions on Software Engineering
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
Adaptive random testing: an illusion of effectiveness?
Proceedings of the 2011 International Symposium on Software Testing and Analysis
Combining search-based and constraint-based testing
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
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One possibility to make testing strategies more effective is to incorporate knowledge about the typical geometric structure of failure-causing inputs within the input domain into the test data selection. For example Adaptive Random Testing is a testing strategy which is based on the idea of failure-causing inputs being clustered within the input domain. So far, there has been no empirical quantification about the location and the geometric shape of failure-causing inputs. Thus it is currently unknown whether the encouraging results gained by Adaptive Random Testing hold true in general. This work aims at introducing an approach which makes it possible to verify the assumption of clustered failure patterns. Possibly it furthermore enables the improvement of current Adaptive Random Testing methods and the development of further black box testing strategies incorporating knowledge about location and shape of failure patterns into test data selection. Therefore metrics for location and shape of failure patterns are specified. They are based on methods from image analysis.