Papers for

scheduling algorithm developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Mining timed automata efficiently by simplifying input behaviors

Mining DTA with SMT by Exploiting Simple Elementary Language and Timed Augmented Prefix Acceptor

Abstract: Timed automata, which extend finite state automata by introducing clock variables, serve as a popular formalism for specifying and analyzing the timed behaviors of real-time systems. Extracting the timed behaviors of a black-box, safety-critical system is crucial for designing and analyzing its real-time requirements, yet it remains challenging. In this paper, we address this problem by generating a deterministic timed automaton (DTA) consistent with a given set of system behaviors, comprising both positive and negative examples. To this end, we adapt the formalism of simple elementary languages (sEL) and introduce the timed augmented prefix tree acceptor (tAPTA). Our approach proceeds as follows: First, we preprocess samples by translating them into sEL, which discards redundancy and detects conflicts; then, we rewrite the resulting sELs in an incremental form and construct a tAPTA to further simplify the samples; finally, we encode the search for a DTA that accepts the simplified tAPTA as an SMT formula. We evaluate our approach on randomly generated benchmarks and a scheduling case study. The results demonstrate the effectiveness of our simplification method in reducing the size of the encoded SMT formula and the efficiency of our approach in mining a DTA.

Tue 15 SeptFormal Languages and Automata Theory
The gist
Understanding how a system works when timing is important can be tricky, especially if you only observe how it behaves without knowing its inner details. The authors propose a way to create a simple model called a deterministic timed automaton that fits what the system does, using examples of both allowed and forbidden behaviors. They simplify the input data by removing redundant and conflicting information before building the model, making the search easier and faster. Their experiments show this method can find these models efficiently.
Open → 2609.16866v1