Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we present a simple steady-state strategy, Pareto Converging Genetic Algorithm (PCGA), which naturally samples the solution space and ensures population advancement towards the Pareto-front. PCGA eliminates the need for sharing/niching and thus minimizes heuristically chosen parameters and procedures. A systematic approach based on histograms of rank is introduced for assessing convergence to the Pareto-front, which, by definition, is unknown in most real search problems. We argue that there is always a certain inheritance of genetic material belonging to a population, and there is unlikely to be any significant gain beyond some point; a stopping criterion where terminating the computation is suggested. For further encouraging diversity and competition, a nonmigrating island model may optionally be used; this approach is particularly suited to many difficult (real-world) problems, which have a tendency to get stuck at (unknown) local minima. Results on three benchmark problems are presented and compared with those of earlier approaches. PCGA is found to produce diverse sampling of the Pareto-front without niching and with significantly less computational effort

No abstract

No abstract

In software engineering, system modeling is the process of formulating a representation of a real system in an abstract way to understand its behavior. Software testing encourages reusing these models for testing purpose. This expedites the process of test case generation. UML structural and behavioral specification diagrams have been used by testing researchers for generation of test scenarios and test data.The aim of this survey is to improve the understanding of UML based testing techniques. We have focused on test case generation from the behavioral specification diagrams, namely sequence, state chart and activity diagrams. We classify the various research approaches that are based on formal specifications, graph theoretic, heuristic testing, and direct UML specification processing. We discuss the issues of test coverage associated with these approaches.

Concurrent programming is increasingly being used in many applications with the advent of multi-cores. The necessary support for execution of multi-threading is getting richer. Notwithstanding, a concurrent program may behave nondeterministically, it may result in different outputs with the same input in different runs.The aim of this study is to generate test sequences for concurrency from unified modelling language (UML) behavioral models such as sequence and activity diagrams. Generating exhaustive test cases for all concurrent interleaving sequences is exponential in size. Therefore, it is necessary to find adequate test cases in presence of concurrency to uncover errors due to, e.g., data race, synchronization and deadlocks. In order to generate adequate test cases a novel search algorithm, which we call concurrent queue search (CQS) is proposed. The CQS handles random nature of concurrent tasks. To generate test scenarios, a sequence diagram is converted into an activity diagram. An activity diagram encapsulates sequential, conditional, iterative and concurrent flows of the control. By the experimental results, it was observed that test sequences generated by CQS algorithm are superior as compared to DFS and BFS search algorithms.