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partabian J, rafe V, parvin H, nejatian S, bagherifard K. Reachability checking in complex and concurrent software systems using intelligent search methods. JSDP 2022; 19 (1) : 12
URL: http://jsdp.rcisp.ac.ir/article-1-1075-en.html
arak university
Abstract:   (1006 Views)
The model checking technique is a formal and effective method for verifying software systems, which analyses it via generating and examining all possible states of a model of the software system. In safety-critical systems, one could not admit the risk of error even in the testing process, therefore it is necessary to carry out the verification process before implementation and at the model level. Using this technique to evaluate properties such as security entails all available states (all state space) being generated, then the state space of the system in question be carefully examined. The main challenge of the model checking technique in large and complex systems with wide or infinite state space is the problem of state space explosion (lack of memory in the generation of all possible states). Graph transformation systems are one of the most widely used formal modeling systems and a suitable solution for modeling and checking complex systems. In systems where security property verification is not possible, the security feature can be refuted by searching for an accessible mode in which a specific configuration (e.g. error or undesirable behavior) occurs. Recent studies advocate that partial and intelligent exploration of part of the state space could be a good solution to the problem of state space explosion. The goal of this study is to use the random forest algorithm in the model checking which can solve the problem of state space explosion by selecting a few promising paths. A path is hopeful whenever the probability of reaching an answer through this path is higher than other paths. In the proposed method, a small model of the system is first created using the official language of the Graph Description System (GTS). Afterwards, a training data set of paths to the goal is generated from the small model mode space. The generated training data set is then provided to the random forest algorithm to identify and discover the logical relationships within it. In the next stage, the acquired knowledge is used to intelligently explore the incomplete space of the large model state. The proposed approach is used in the verification of the reachability property and to refute the safety feature in large and complex systems where it is impossible to generate the entire system state space. In order to evaluate the proposed approach, it has been implemented in GROOVE which is an open source tool for designing and checking models in graph conversion systems. The results indicate that the proposed method performs better than the compared methods in terms of average running time and the length of the generated witness.
Article number: 12
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Type of Study: Research | Subject: Paper
Received: 2019/09/27 | Accepted: 2021/03/1 | Published: 2022/06/22 | ePublished: 2022/06/22

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