Testing object-oriented systems: models, patterns, and tools
Testing object-oriented systems: models, patterns, and tools
Test Case Prioritization: A Family of Empirical Studies
IEEE Transactions on Software Engineering
ISSRE '03 Proceedings of the 14th International Symposium on Software Reliability Engineering
COMPSAC '06 Proceedings of the 30th Annual International Computer Software and Applications Conference - Volume 02
Practical Model-Based Testing: A Tools Approach
Practical Model-Based Testing: A Tools Approach
The Current State and Future of Search Based Software Engineering
FOSE '07 2007 Future of Software Engineering
Search Algorithms for Regression Test Case Prioritization
IEEE Transactions on Software Engineering
An Empirical Study of Test Case Filtering Techniques Based on Exercising Information Flows
IEEE Transactions on Software Engineering
PHALANX: a graph-theoretic framework for test case prioritization
Proceedings of the 2008 ACM symposium on Applied computing
ARTOO: adaptive random testing for object-oriented software
Proceedings of the 30th international conference on Software engineering
Proceedings of the eighteenth international symposium on Software testing and analysis
Adaptive Random Testing: The ART of test case diversity
Journal of Systems and Software
Adaptive Random Test Case Prioritization
ASE '09 Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering
Using String Distances for Test Case Prioritisation
ASE '09 Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering
A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search
IEEE Transactions on Software Engineering
An enhanced test case selection approach for model-based testing: an industrial case study
Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering
IEEE Transactions on Software Engineering
On the use of a similarity function for test case selection in the context of model-based testing
Software Testing, Verification & Reliability
Test-Suite reduction using genetic algorithm
APPT'05 Proceedings of the 6th international conference on Advanced Parallel Processing Technologies
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Adaptive random testing: an illusion of effectiveness?
Proceedings of the 2011 International Symposium on Software Testing and Analysis
Prioritizing test cases with string distances
Automated Software Engineering
Pairwise testing for software product lines: comparison of two approaches
Software Quality Control
Achieving scalable model-based testing through test case diversity
ACM Transactions on Software Engineering and Methodology (TOSEM)
An orchestrated survey of methodologies for automated software test case generation
Journal of Systems and Software
Static test case prioritization using topic models
Empirical Software Engineering
Hi-index | 0.00 |
Model-based testing (MBT) suffers from two main problems which in many real world systems make MBT impractical: scalability and automatic oracle generation. When no automated oracle is available, or when testing must be performed on actual hardware or a restricted-access network, for example, only a small set of test cases can be executed and evaluated. However, MBT techniques usually generate large sets of test cases when applied to real systems, regardless of the coverage criteria. Therefore, one needs to select a small enough subset of these test cases that have the highest possible fault revealing power. In this paper, we investigate and compare various techniques for rewarding diversity in the selected test cases as a way to increase the likelihood of fault detection. We use a similarity measure defined on the representation of the test cases and use it in several algorithms that aim at maximizing the diversity of test cases. Using an industrial system with actual faults, we found that rewarding diversity leads to higher fault detection compared to the techniques commonly reported in the literature: coverage-based and random selection. Among the investigated algorithms, diversification using Genetic Algorithms is the most cost-effective technique.