Multi-objective construction of an entire adequate test suite for an EFSM

In this paper we propose a method and a tool to generate test suites from extended finite state machines, accounting for multiple (potentially conflicting) objectives. We aim at maximizing coverage and feasibility of a test suite while minimizing similarity between its test cases and minimizing overall cost. Therefore, we define a multi-objective genetic algorithm that searches for optimal test suites based on four objective functions. In doing so, we create an entire test suite at once as opposed to test cases one at a time. Our approach is evaluated on two different case studies, showing interesting initial results

A multi-objective genetic algorithm for generating test suites from extended finite state machines

We propose a test suite generation technique from extended finite state machines based on a genetic algorithm that fulfills multiple (conflicting) objectives. We aim at maximizing coverage and feasibility of a set of test cases while minimizing similarity between these cases and minimizing overall cost

Multi-objective construction of an entire adequate test suite for an EFSM

In this paper we propose a method and a tool to generate test suites from extended finite state machines, accounting for multiple (potentially conflicting) objectives. We aim at maximizing coverage and feasibility of a test suite while minimizing similarity between its test cases and minimizing overall cost. Therefore, we define a multi-objective genetic algorithm that searches for optimal test suites based on four objective functions. In doing so, we create an entire test suite at once as opposed to test cases one at a time. Our approach is evaluated on two different case studies, showing interesting initial results

On the Effect of Counters in Guard Conditions When State-Based Multi-objective Testing

During test case generation from an extended finite state machine (EFSM), the counter problem is caused by the presence of guard conditions that refer to counter variables. Because such variables are initialized and updated by transitions in the EFSM, every traversal of the state machine graph is not necessarily feasible, i.e., executable. The problem manifests itself by the fact that a transition, a sequence of transitions, or a more complex behavior in the state machine, has to be repeatedly triggered to eventually trigger a specific behavior (another transition). In this paper we define different manifestations of the counter problem and experiment with a new search based solution for that problem. We also investigate how the counter problem affects a multi-objective genetic algorithm that generates test suites from an EFSM. We evaluate our solution and compare it with an existing one, using three different case studies