Multi-objective optimization algorithms applied to the class integration and test order problem

In the context of object-oriented software, a common problem is the determination of test orders for the integration test of classes, known as the class integration and test order (CITO) problem. The existing approaches, based on graphs, usually generate solutions that are sub-optimal, and do not co...

Full description

Saved in:
Bibliographic Details
Published inInternational journal on software tools for technology transfer Vol. 14; no. 4; pp. 461 - 475
Main Authors Vergilio, Silvia Regina, Pozo, Aurora, Árias, João Carlos Garcia, da Veiga Cabral, Rafael, Nobre, Tiago
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer-Verlag 01.08.2012
Springer Nature B.V
Subjects
Algorithms
Ant colony optimization
Complex systems
Computer programs
Computer Science
Cost function
Genetic algorithms
Ictss 2010
Object oriented programming
Optimization
Optimization algorithms
Software
Software Engineering
Software Engineering/Programming and Operating Systems
Theory of Computation
Object-oriented software
Multi-objective optimization
Integration test
Online AccessGet full text

Cover

More Information
Summary:In the context of object-oriented software, a common problem is the determination of test orders for the integration test of classes, known as the class integration and test order (CITO) problem. The existing approaches, based on graphs, usually generate solutions that are sub-optimal, and do not consider the different factors and measures that can affect the construction of stubs. 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 solution, is not always a trivial task, mainly for complex systems. Therefore, to better represent the CITO problem, we introduce, in this paper, a multi-objective optimization approach, to generate a set of good solutions that achieve a balanced compromise between the different measures (objectives). Three different multi-objective optimization algorithms (MOA) were implemented: Pareto ant colony, multi-objective Tabu search and non-dominated sorting GA. The approach is applied to real programs and the obtained results allow comparison with the simple GA approach and evaluation of the different MOA.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1433-2779
1433-2787
DOI:10.1007/s10009-012-0226-1