Incremental testing of object-oriented class structures
ICSE '92 Proceedings of the 14th international conference on Software engineering
Testing object-oriented systems: models, patterns, and tools
Testing object-oriented systems: models, patterns, and tools
Using genetic algorithms and coupling measures to devise optimal integration test orders
SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
Test Order for Inter-Class Integration Testing of Object-Oriented Software
COMPSAC '97 Proceedings of the 21st International Computer Software and Applications Conference
An Investigation of Graph-Based Class Integration Test Order Strategies
IEEE Transactions on Software Engineering
Software Engineering: A Practitioner's Approach
Software Engineering: A Practitioner's Approach
A Test Strategy for Object-Oriented Programs
COMPSAC '95 Proceedings of the 19th International Computer Software and Applications Conference
Coupling-based class integration and test order
Proceedings of the 2006 international workshop on Automation of software test
The Current State and Future of Search Based Software Engineering
FOSE '07 2007 Future of Software Engineering
An empirical study of cycles among classes in Java
Empirical Software Engineering
Solving a bi-objective flowshop scheduling problem by pareto-ant colony optimization
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Establishing integration test orders of classes with several coupling measures
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Integration test of classes and aspects with a multi-evolutionary and coupling-based approach
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
Controversy Corner: Search Based Software Engineering: Review and analysis of the field in Brazil
Journal of Systems and Software
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In the context of Object-Oriented software, many works have investigated the Class Integration and Test Order (CITO) problem, proposing solutions to determine test orders for the integration test of the program classes. The existing approaches based on graphs can generate solutions that are sub-optimal, and do not consider the different factors and measures that can affect the stubbing process. To overcome this limitation, solutions based on Genetic Algorithms (GA) have presented promising results. However, the determination of a cost function, which is able to generate the best solutions, is not always a trivial task, mainly for complex systems with a great number of measures. Therefore, we introduce, in this paper, a multi-objective optimization approach to better represent the CITO problem. The approach generates a set of good solutions that achieve a balanced compromise between the different measures (objectives). It was implemented by a Pareto Ant Colony (P-ACO) algorithm, which is described in detail. The algorithm was used in a set of real programs and the obtained results are compared to the GA results. The results allow discussing the difference between single and multi-objective approaches especially for complex systems with a greater number of dependencies among the classes.