提取,建模,改进:通过数据丰富改进程序验证工具的改进建模
Sophie Lathouwers1, Yujie Liu1, Vadim Zaytsev1
1Formal Methods and Tools, University of Twente, Enschede, The Netherlands.
概括
本研究介绍了一种大模型,用于对400多个程序验证工具进行分类. 精选的数据集有助于软件工程师选择和比较系统正确性的工具.
科学领域:
- 软件工程 软件工程 软件工程
- 正式方法 正式方法
- 计算机科学 计算机科学
背景情况:
- 在软件工程中,模型对于推理系统正确性至关重要.
- 程序验证技术提供不同程度的正确性保证.
- 需要一个结构化的方法来驾各种各样的验证工具.
研究的目的:
- 为分类程序验证工具开发一个简洁的超级模型.
- 提供400多个程序验证工具的综合数据集.
- 为软件工程师方便工具选择,比较和趋势识别.
主要方法:
- 调查了程序验证工具的领域.
- 开发了一个工具分类的巨型模型.
- 编制了一个数据集,包含工具类别和实用信息 (例如输入/输出,存储库链接).
- 自动数据提取和使用API进行数据维护.
主要成果:
- 一个巨型模型,区分各种程序验证工具.
- 一个公开可用的数据集,包含400多个工具和详细的分类.
- 识别程序验证工具领域的趋势.
- 自动化数据维护增强了数据集可扩展性.
结论:
- 大模型和数据集为软件工程师提供了宝贵的资源.
- 分类有助于找到,调查和比较合适的验证工具.
- 数据集支持更容易进入程序验证和基于特定要求的知情工具选择.
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