Comparing Metaheuristic Algorithms for Error Detection in Java Programs

Chicano, Francisco; Ferreira, M.; Alba, Enrique

doi:10.1007/978-3-642-23716-4_11

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…For example, the recent work by Chicano et al [25] evaluates several meta-heuristic model checking techniques using communication protocols. Similarly, Shousha et al [32] uses models of a bank fund transfer problem and a cruise control problem.…”

Section: Discussionmentioning

confidence: 99%

“…In the paper [24], Ferreira et al presented a novel technique based on Particle Swarm Optimization (PSO) that finds safety violations. Chicano et al compared several deterministic and nondeterministic software model checking algorithms using Java PathFinder [25]. Their target algorithms include DFS, BFS, A*, GA, ACO, PSO, Simulated Annealing, Random Search, and Beam Search.…”

Section: Challenges and Related Workmentioning

confidence: 99%

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Ant Colony Optimization Based Model Checking Extended by Smell-like Pheromone

Kumazawa¹,

Yokoyama²,

Takimoto³

et al. 2016

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Model Checking is a technique for automatically checking the model representing software or hardware about whether they satisfy the corresponding specifications. Traditionally, the model checking uses deterministic algorithms, but the deterministic algorithms have a fatal problem. They are consuming too many computer resources. In order to mitigate the problem, an approach based on the Ant Colony Optimization (ACO) was proposed. Instead of performing exhaustive checks on the entire model, the ACO based approach statistically checks a part of the model through movements of ants (ant-like software agents). Thus the ACO based approach not only suppresses resource consumption, but also guides the ants to reach the goals efficiently. The ACO based approach is known to generate shorter counter examples too. This article presents an improvement of the ACO based approach. We employ a technique that further suppresses futile movements of ants while suppressing the resource consumption by introducing a smell-like pheromone. While ACO detects the semishortest path to food by putting pheromones on the trails of ants, the smell-like pheromone diffuses differently from the traditional pheromone. In our approach, the smell-like pheromone diffuses from food, and guides ants to the food. Thus our approach not only makes the ants reach the goals farther and more efficiently but also generates much shorter counter examples than those of the traditional approaches. In order to demonstrate the effectiveness of our approach, we have implemented our approach on a practical model checker, and conducted numerical experiments. The experimental results show that our approach is effective for improving execution efficiency and the length of counter examples.

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Section: Discussionmentioning

confidence: 99%

Section: Challenges and Related Workmentioning

confidence: 99%

Ant Colony Optimization Based Model Checking Extended by Smell-like Pheromone

Kumazawa¹,

Yokoyama²,

Takimoto³

et al. 2016

EAI Endorsed Transactions on Industrial Networks and Intelligen

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“…Hence, these works concentrate on searching for a walk in a directed graph representing the state space generated by a model checker where the walk starts in an initial state and ends in an error state. Various meta‐heuristics, including simulated annealing , the genetic algorithm , the partial swarm optimization (PSO) , and the ant colony optimization (ACO) , have successfully been used within GMC to find deadlocks and/or assertion violations in simple concurrent programmes and protocols. An advantage of GMC is that the underlying model checking offers a well‐defined state space and a high degree of systematic approach.…”

Section: Meta‐heuristic Setting Of Test and Noise Parametersmentioning

confidence: 99%

Advances in noise‐based testing of concurrent software

Fiedor

Hrubá

Křena

et al. 2014

Software Testing Verif & Rel

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SummaryTesting of concurrent software written in programming languages like Java and C/C++ is a highly challenging task owing to the many possible interactions among threads. A simple, cheap, and effective approach that addresses this challenge is testing with noise injection, which influences the scheduling so that different interleavings of concurrent actions are witnessed. In this paper, multiple results achieved recently in the area of noise‐injection‐based testing by the authors are presented in a unified and extended way. In particular, various concurrency coverage metrics are presented first. Then, multiple heuristics for solving the noise placement problem (i.e. where and when to generate noise) as well as the noise seeding problem (i.e. how to generate the noise) are introduced and experimentally evaluated. In addition, several new heuristics are proposed and included into the evaluation too. Recommendations on how to set up noise‐based testing for particular scenarios are then given. Finally, a novel use of the genetic algorithm for finding suitable combinations of the many parameters of tests and noise techniques is presented. Copyright © 2014 John Wiley & Sons, Ltd.

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