Towards New Methods for Developing Real-Time Systems: Automatically Deriving Loop Bounds Using Machine Learning
Towards New Methods for Developing Real-Time Systems: Automatically Deriving Loop Bounds Using Machine Learning
复制标题
开发实时系统的新方法：使用机器学习自动导出循环边界
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
I. Bate
中科院分区：
文献类型：
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作者：
D. Kazakov;I. Bate
软件工程中的大多数开发，验证和验证方法都需要采用适当信息的某种形式的模型。实时系统也不例外。但是，一个重要的问题是，所需的信息并不总是可用。通常，这些信息是使用手动方法得出的，这在时间和金钱方面是昂贵的。在本文中，我们展示了从其他领域采集的技术如何提供更有效和有效的解决方案。更具体地说，机器学习应用于自动得出循环边界的问题。该论文显示了如何基于机器学习的方法可以相对轻松地解决困难的问题。
Most development, verification and validation methods in software engineering require some form of model populated with appropriate information. Realtime systems are no exception. However a significant issue is that the information needed is not always available. Often this information is derived using manual methods, which is costly in terms of time and money. In this paper we show how techniques taken from other areas may provide more effective and efficient solutions. More specifically machine learning is applied to the problem of automatically deriving loop bounds. The paper shows how taking an approach based on machine learning allows a difficult problem to be addressed with relative ease.