Behavioral Analysis of Timed Actors using Syntactic Slice Equivalence

2026-09-15

AI Summary

本文提出了一种静态分析方法，通过比较Rebeca依赖图的后向切片来判断定时行为模型是否可以重用，从而避免了重新生成模型的高成本。

Abstract

Tiny twins are compact behavioral models derived from timed actor models for selected observable messages. When a source model evolves, regenerating its tiny twin requires state-space exploration and reduction even if the relevant behavior is unchanged. We present a static analysis for Timed Rebeca that compares backward slices of Rebeca dependence graphs for a given set of observable message names. For the Zeno-free fragment with after annotations and no delay statements, we prove that slice equivalence implies weak timed bisimulation under the selected observations. This preserves observable actions and total elapsed time across internal transitions, allowing the existing tiny twin to be reused. We evaluate the implementation on ten benchmark models paired with revisions that preserve their observable slices. The cost of static comparison depends on the size of the graphs representing source statements and their dependencies, while tiny-twin generation depends on the number of reachable states and transitions. This difference is reflected in the measurements: static comparison takes less than one second using tens of megabytes of memory, while tiny-twin generation can take over an hour and use hundreds of gigabytes.

Problem

• Timed Actors
• Behavioral Models
• State-space Exploration
• Static Analysis
• Observable Messages

Innovation

• static analysis
• slice equivalence
• timed bisimulation
• behavioral model
• state-space exploration