Ana Ţurlea
ABSTRACT
Finite state machines (FSMs) are widely used in test data generation approaches. An extended finite state machine (EFSM) extends the FSM with memory (context variables), guards for each transition and assignment operations. In FSMs all paths are feasible, but the existence of context variables combined with guards in EFSMs can lead to infeasible paths. Using EFSMs in test data generation, we are dealing with feasibility problems. This paper presents a test suite generation algorithm for EFSMs. The algorithm produces a set of feasible transition paths (test cases) that cover all transitions using NSGA-III. We also measure the similarities between test cases from the generated test suite.
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Index Terms
- Testing extended finite state machines using NSGA-III
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Reviews
Angelica de Antonio
The goal of the paper is to describe how the non-dominated sorting genetic algorithm III (NSGA-III) algorithm generates test suites from extended finite-state machines (EFSMs). Each generated test case represents a feasible transition path on the EFSM. The ability to deal with feasibility is one advantage of using EFSMs instead of FSMs, thanks to the use of context variables and guards associated with transitions. NSGA-III is a genetic algorithm that has been applied to multi-objective problems. In its application for test case generation, three objectives are defined: "one objective for estimating the feasibility of each path, one objective for covering all transitions and minimizing the necessary paths, and the third objective [is the] minimization of similarities between tests." Chromosomes encode variable-length sets of paths where each gene is a list of integer numbers representing transitions. The solutions of the genetic algorithm are "those chromosomes that have only feasible paths and cover all transitions." An experiment was run with two different EFSMs, comparing the performance of the proposed algorithm with a previous proposal based on NSGA-II. The computation time was lower and the success rate (feasible and full coverage solutions) was higher. The paper is inspired by Kalaji et al. [1] and Asoudeh and Labiche [2], and is an evolution of two previous works by urlea published in 2017 and 2018. This clear and concise paper mainly addresses researchers in automated test case generation.
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