A methodology for controlling the size of a test suite
ACM Transactions on Software Engineering and Methodology (TOSEM)
Incremental program testing using program dependence graphs
POPL '93 Proceedings of the 20th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Analyzing Regression Test Selection Techniques
IEEE Transactions on Software Engineering
Experiments of the effectiveness of dataflow- and controlflow-based test adequacy criteria
ICSE '94 Proceedings of the 16th international conference on Software engineering
Effect of test set minimization on fault detection effectiveness
Software—Practice & Experience
Prioritizing test cases for regression testing
Proceedings of the 2000 ACM SIGSOFT international symposium on Software testing and analysis
Incorporating varying test costs and fault severities into test case prioritization
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Meeting challenges and surviving success: the 2nd workshop on open source software engineering
Proceedings of the 24th International Conference on Software Engineering
Approximation algorithms for combinatorial problems
STOC '73 Proceedings of the fifth annual ACM symposium on Theory of computing
Reducing the cost of regression testing by semantics guided test case selection
ICSM '95 Proceedings of the International Conference on Software Maintenance
An Empirical Study of the Effects of Minimization on the Fault Detection Capabilities of Test Suites
ICSM '98 Proceedings of the International Conference on Software Maintenance
Test Case Prioritization: An Empirical Study
ICSM '99 Proceedings of the IEEE International Conference on Software Maintenance
Empirical Studies of Test Case Prioritization in a JUnit Testing Environment
ISSRE '04 Proceedings of the 15th International Symposium on Software Reliability Engineering
Empirical Software Engineering
TimeAware test suite prioritization
Proceedings of the 2006 international symposium on Software testing and analysis
Search Algorithms for Regression Test Case Prioritization
IEEE Transactions on Software Engineering
Pareto efficient multi-objective test case selection
Proceedings of the 2007 international symposium on Software testing and analysis
Search based software engineering: techniques, taxonomy, tutorial
Empirical Software Engineering and Verification
Information and Software Technology
On-demand test suite reduction
Proceedings of the 34th International Conference on Software Engineering
Cost-aware pareto optimal test suite minimisation for service-centric systems
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Search based constrained test case selection using execution effort
Expert Systems with Applications: An International Journal
Multi-objective test case prioritization for GUI applications
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Test suite minimisation techniques seek to reduce the effort required for regression testing by selecting a subset of test suites. In previous work, the problem has been considered as a single-objective optimisation problem. However, real world regression testing can be a complex process in which multiple testing criteria and constraints are involved. This paper presents the concept of Pareto efficiency for the test suite minimisation problem. The Pareto-efficient approach is inherently capable of dealing with multiple objectives, providing the decision maker with a group of solutions that are not dominated by each other. The paper illustrates the benefits of Pareto efficient multi-objective test suite minimisation with empirical studies of two and three objective formulations, in which multiple objectives such as coverage and past fault-detection history are considered. The paper utilises a hybrid, multi-objective genetic algorithm that combines the efficient approximation of the greedy approach with the capability of population based genetic algorithm to produce higher-quality Pareto fronts.