Finding short counterexamples in promela models using estimation of distribution algorithms

Staunton, Jan; Clark, John A.

doi:10.1145/2001576.2001834

Cited by 12 publications

(3 citation statements)

References 14 publications

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“…There has already been some initial work on ways of searching for derived probability distributions for statistical testing [32] and for inferring strategies from paths in model checking [33]. There has also been work on the search for tactics for program transformation [34], [35].…”

Section: A Searching For Strategies Rather Than Instancesmentioning

confidence: 99%

The role of Artificial Intelligence in Software Engineering

Harman

2012

2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE)

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Abstract-There has been a recent surge in interest in the application of Artificial Intelligence (AI) techniques to Software Engineering (SE) problems. The work is typified by recent advances in Search Based Software Engineering, but also by long established work in Probabilistic reasoning and machine learning for Software Engineering. This paper explores some of the relationships between these strands of closely related work, arguing that they have much in common and sets out some future challenges in the area of AI for SE.

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Section: A Searching For Strategies Rather Than Instancesmentioning

confidence: 99%

The role of Artificial Intelligence in Software Engineering

Harman

2012

2012 First International Workshop on Realizing AI Synergies in Software Engineering (RAISE)

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“…Alternatively, it is possible to employ additional analyses or meta-heuristics to select the best coefficients for h T I and h CI . For example, we recommend using a linear estimation-of-distribution algorithm as defined by Staunton and Clark 2011 [48], or using static analysis to make a list of bug candidates to determine what classes of bugs are most likely present in the program. However, a treatment of that subject goes beyond the scope of this paper.…”

Section: T Icimentioning

confidence: 99%

Refinement of structural heuristics for model checking of concurrent programs through data mining

Milewicz

Pirkelbauer

2017

Computer Languages, Systems & Structures

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“…Staunton et al proposed the heuristic search algorithms for model checking based on Estimation of Distribution Algorithm [26,27]. Yousefian et al proposed a safety checking technique for graph transformation systems based on GA to deal with the state explosion problem.…”

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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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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Finding short counterexamples in promela models using estimation of distribution algorithms

Cited by 12 publications

References 14 publications

The role of Artificial Intelligence in Software Engineering

The role of Artificial Intelligence in Software Engineering

Refinement of structural heuristics for model checking of concurrent programs through data mining

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

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