论文标题
基于流的监视语言的自动优化
Automatic Optimizations for Stream-based Monitoring Languages
论文作者
论文摘要
在基于流的监视语言中指定的运行时监视器往往比用标准编程语言编写的监视更容易理解，维护和重复使用。由于其正式的语义，这种规范语言也是对安全至关重要应用的自然选择。与标准编程语言不同，到目前为止，对自动代码优化的支持很少。在本文中，我们介绍了基于流的监视语言Rtlola的第一个代码转换集合。我们表明，经典的编译器优化，例如稀疏的条件恒定传播和消除常见的亚表达，可以适用于监视规范。我们还开发了新的转换 - 起搏类型的改进和过滤器改进 - 利用了rtlola的特定模块化结构以及声明性规范语言提供的实现自由。我们证明了对无人飞机系统（UAS）监控基准的代码转换对基准的重大影响。
Runtime monitors that are specified in a stream-based monitoring language tend to be easier to understand, maintain, and reuse than those written in a standard programming language. Because of their formal semantics, such specification languages are also a natural choice for safety-critical applications. Unlike for standard programming languages, there is, however, so far very little support for automatic code optimization. In this paper, we present the first collection of code transformations for the stream-based monitoring language RTLola. We show that classic compiler optimizations, such as Sparse Conditional Constant Propagation and Common Subexpression Elimination, can be adapted to monitoring specifications. We also develop new transformations -- Pacing Type Refinement and Filter Refinement -- which exploit the specific modular structure of RTLola as well as the implementation freedom afforded by a declarative specification language. We demonstrate the significant impact of the code transformations on benchmarks from the monitoring of unmanned aircraft systems (UAS).
