Inferring automata-based programs from specification with mutation-based ant colony optimization

D. Chivilikhin, V. Ulyantsev
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引用次数: 6

Abstract

In this paper we address the problem of constructing correct-by-design programs with the use of the automata-based programming paradigm. A recent algorithm for learning finite-state machines (FSMs) MuACOsm is applied to the problem of inferring extended finite-state machine (EFSM) models from behavior examples (test scenarios) and temporal properties, and is shown to outperform the genetic algorithm (GA) used earlier.
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用基于突变的蚁群优化从规范中推断基于自动机的程序
在本文中,我们讨论了使用基于自动机的编程范式来构造设计正确的程序的问题。最近一种用于学习有限状态机(fsm)的算法MuACOsm被应用于从行为示例(测试场景)和时间属性推断扩展有限状态机(EFSM)模型的问题,并被证明优于先前使用的遗传算法(GA)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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