Analyzing Regression Test Selection Techniques
IEEE Transactions on Software Engineering
A safe, efficient regression test selection technique
ACM Transactions on Software Engineering and Methodology (TOSEM)
Prioritizing Test Cases For Regression Testing
IEEE Transactions on Software Engineering
Test Case Prioritization: A Family of Empirical Studies
IEEE Transactions on Software Engineering
The Cow_Suite Approach to Planning and Deriving Test Suites in UML Projects
UML '02 Proceedings of the 5th International Conference on The Unified Modeling Language
Specification-based regression test selection with risk analysis
CASCON '02 Proceedings of the 2002 conference of the Centre for Advanced Studies on Collaborative research
Object distance and its application to adaptive random testing of object-oriented programs
Proceedings of the 1st international workshop on Random testing
An evaluation of combination strategies for test case selection
Empirical Software Engineering
Pareto efficient multi-objective test case selection
Proceedings of the 2007 international symposium on Software testing and analysis
LTS-BT: a tool to generate and select functional test cases for embedded systems
Proceedings of the 2008 ACM symposium on Applied computing
Coverage-driven automatic test generation for uml activity diagrams
Proceedings of the 18th ACM Great Lakes symposium on VLSI
Automated Scenario Generation Based on UML Activity Diagrams
ICIT '08 Proceedings of the 2008 International Conference on Information Technology
Proceedings of the eighteenth international symposium on Software testing and analysis
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
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Full testing involves running all the tests in the test suite. This is exhaustive and will consume an inordinate amount of time and money. Hence, an ordering of test cases aids in early detection of faults. However, ordering and running a large test suite is still infeasible, as it would not be possible to run all tests during regression testing. In this work, clustering is used to select a subset of scenarios for testing. First, a distance matrix is obtained by using Levenshtein distance to compare scenarios. This distance matrix is used as input for the Agglomerative Hierarchical Clustering(AHC) technique with the objective of selecting dissimilar test scenarios and at the same time achieveing maximum coverage and rate of fault detection.