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首页> 外文期刊>Soft computing: A fusion of foundations, methodologies and applications >An approach based on knowledge exploration for state space management in checking reachability of complex software systems
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An approach based on knowledge exploration for state space management in checking reachability of complex software systems

机译：一种基于知识探索的国家空间管理在检查复杂软件系统可拆卸性的方法

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摘要

Model checking is one of the most efficient techniques in software system verification. However, state space explosion is a big challenge while using this technique to check different properties like safety ones. In this situation, one can search the state space to find a reachable state in which the safety property is violated. Hence, reachability checking can be done instead of checking safety property. However, checking reachability in the worst case may cause state space explosion again. To handle this problem, our idea is based on generating a small model consistent with the main model. Then by exploring the state space entirely, we search it to find the goal states. After finding the goal states, we label the paths which start from the initial state and leading to a goal state. Then using the ensemble classification technique, the necessary knowledge is extracted from these paths to intelligently explore the state space of the bigger model. Ensemble machine learning technique uses Boosting method along with decision trees. It follows sampling techniques by replacement. This method generates k predictive models after sampling k times. Finally, it uses a voting mechanism to predict the labels of the final path. Our proposed approach is implemented in GROOVE, which is an open source toolset for designing and model checking graph transformation systems. Our experiments show a significant improvement in terms of both speed and memory usage. In average, our approach consumes nearly 42% fewer memory than other approaches. Also, it generates witnesses nearly 20% shorter than others, in average.
机译：模型检查是软件系统验证中最有效的技术之一。然而，使用这种技术的同时，状态空间爆炸是一个很大的挑战，以检查像安全性的不同属性。在这种情况下，可以搜索状态空间以找到违反安全性的可靠状态。因此，可以进行可达性检查而不是检查安全性。但是，在最坏情况下检查可达性可能会再次导致状态空间爆炸。为了处理这个问题，我们的想法是基于生成与主模型一致的小型模型。然后通过完全探索国家空间，我们搜索它以找到目标状态。找到目标状态后，我们标记从初始状态启动并导致目标状态的路径。然后使用集合分类技术，从这些路径中提取必要的知识以智能地探索更大模型的状态空间。合奏机学习技术使用升压方法以及决策树。它遵循更换的采样技术。采样后，该方法会产生K预测模型。最后，它使用了投票机制来预测最终路径的标签。我们所提出的方法是在凹槽中实现的，它是用于设计和模型检查图形变换系统的开源工具集。我们的实验表现出速度和内存使用率的显着改善。平均而言，我们的方法消耗了比其他方法更少42％。此外，它平均生成了比其他人短的目击者近20％。

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