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

Abstract

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.

A multi-objective and evolutionary hyper-heuristic applied to the Integration and Test Order Problem

“…On the other hand, if a dependency graph has many cycles, then the problem becomes complex and search-based algorithms are applicable to find good orders. Some SBSE works have applied metaheuristics to solve this problem [4,7,47,48].…”

“…We choose to tackle this problem due to its main characteristics that allow a robust application of hyper-heuristics: i) it is properly solved by multiobjective algorithms [3,4,47]; ii) the representation of the problem is the same as permutation problems, which provides several operators to be selected by the hyper-heuristic; iii) the problem can be addressed in several contexts; and iv) it is a real problem, hence if effectively solved, the engineer effort invested in this activity can be significantly reduced. In this sense, the objective of applying hyper-heuristics in this work is to provide a generic and robust approach to solve this problem, while also outperforming traditional algorithms (such as Genetic Algorithms -GAs) on the minimization of stubbing cost.…”

“…HITO was implemented using jMetal [17], an object-oriented framework for . For all experiments we defined NSGA-II [14] as the MOEA used by HITO, since it is one of the common MOEAs used for solving this problem and within the hyper-heuristics field for comparison [4,28,36,47]. For executing the described algorithms, we used the chromosome representation and the constraints adopted by the approach presented in [4], since this approach was used as a standard approach for the MOEA due to its good results.…”

“…Furthermore, we applied the algorithms on 7 real world object-oriented and aspect-oriented systems. These are well known and widely used systems [4,3,21,47,37,48]…”

Evaluating different strategies for integration testing of aspect-oriented programs

“…Such algorithms were chosen due to two main reasons. First of all, evolutionary algorithms, such as NSGA-II, have presented the best performance in the OO context, when compared with other bio-inspired algorithms, such as PACO and MTabu [31]. The second one is that they implement different evolution mechanisms, and this helps us to investigate the influence of the strategies in the search space.…”