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Learning Concise Models from Long Execution Traces
TL;DR: In this article, the authors describe a new algorithm for automatically extracting useful models, as automata, from execution traces of a HW/SW system driven by software exercising a use-case of interest.
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Abstract: models of system-level behaviour have applications in design exploration, analysis, testing and verification. We describe a new algorithm for automatically extracting useful models, as automata, from execution traces of a HW/SW system driven by software exercising a use-case of interest. Our algorithm leverages modern program synthesis techniques to generate predicates on automaton edges, succinctly describing system behaviour. It employs trace segmentation to tackle complexity for long traces. We learn concise models capturing transaction-level, system-wide behaviour--experimentally demonstrating the approach using traces from a variety of sources, including the x86 QEMU virtual platform and the Real-Time Linux kernel.
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TABLE I: Runtime comparison for segmented and non-segmented trace input. For a fair comparison, we begin learning with number of states equal to N . ![Fig. 1: USB Slot state machine provided in (a) Intel datasheet [10] and (b) model learnt by our framework.](/figures/figure1-1-1sb8fxvimjk6.png)
Fig. 1: USB Slot state machine provided in (a) Intel datasheet [10] and (b) model learnt by our framework. 
Fig. 6: Model of RT-Linux Kernel Thread Scheduling learnt by our framework 
Fig. 7: Graph plot (log–log plot) comparing runtime for segmented and non-segmented trace input for the integrator example. 
TABLE II: Runtime analysis of state-merge vs. model learning. 
Fig. 4: Model of an anti-windup integrator learnt by our framework.
![Fig. 1: USB Slot state machine provided in (a) Intel datasheet [10] and (b) model learnt by our framework.](/figures/figure1-1-1sb8fxvimjk6.webp)
Citations
•Posted Content
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
Mohammadhosein Hasanbeig,Natasha Yogananda Jeppu,Alessandro Abate,Tom Melham,Daniel Kroening +4 more
TL;DR: This work synthesises a human-interpretable automaton from trace data generated through exploration of the environment by the deep RL agent, then enriched with the synthesised automaton so that generation of an optimal control policy by deep RL is guided by the discovered structure encoded in the automaton.
13
System-on-Chip Message Flow Mining with Masked-Language Models
Md Rubel Ahmed,Bardia Nadimi,Hao Zheng +2 more
- 06 Aug 2023
TL;DR: A masked-language model is proposed to infer comprehensive and accurate system-on-chip specifications from communication traces, overcoming complexity and facilitating design understanding and validation, outperforming existing state-of-the-art trace mining tools in experiments.
3
Symbolic Task Inference in Deep Reinforcement Learning
Hosein Hasanbeig,Natasha Yogananda Jeppu,Alessandro Abate,Tom Melham,Daniel Kroening +4 more
TL;DR: This paper proposes DeepSynth, a method for training deep reinforcement learning agents in sparse or non-Markovian environments by synthesizing a compact finite state automaton from trace data, enabling efficient policy synthesis and scalability.
1
Enhancing active model learning with equivalence checking using simulation relations
Natasha Yogananda Jeppu,Tom Melham,Daniel Kroening +2 more
TL;DR: A new active model-learning approach to generating abstractions of a system from its execution traces, which generates an equivalent model for 98% of the Stateflow models.
Mining SoC Message Flows with Attention Model
TL;DR: A dis-ruptive method is proposed that utilizes deep sequence modeling with the attention mechanism to infer accurate specifications from SoC communication traces and outperforms several existing state-of-the-art trace mining tools.
References
Learning regular sets from queries and counterexamples
TL;DR: In this article, the problem of identifying an unknown regular set from examples of its members and nonmembers is addressed, where the regular set is presented by a minimaMy adequate teacher, which can answer membership queries about the set and can also test a conjecture and indicate whether it is equal to the unknown set and provide a counterexample if not.
A Tool for Checking ANSI-C Programs
Edmund M. Clarke,Daniel Kroening,Flavio Lerda +2 more
- 29 Mar 2004
TL;DR: The tool supports almost all ANSI-C language features, including pointer constructs, dynamic memory allocation, recursion, and the float and double data types, and is integrated into a graphical user interface.
Complexity of automaton identification from given data
TL;DR: The question of whether there is an automaton with n states which agrees with a finite set D of data is shown to be NP-complete, although identification-in-the-limit of finite automata is possible in polynomial time as a function of the size of D.
919
On the Synthesis of Finite-State Machines from Samples of Their Behavior
A. W. Biermann,Jerome A. Feldman +1 more
TL;DR: The Nerode realization technique for synthesizing finite-state machines from their associated right-invariant equivalence relations is modified to give a method for synthesizer machines from finite subsets of their input-output behavior.
593
Syntax-guided synthesis
Rajeev Alur,Rastislav Bodik,Garvit Juniwal,Milo M. K. Martin,Mukund Raghothaman,Sanjit A. Seshia,Rishabh Singh,Armando Solar-Lezama,Emina Torlak,Abhishek Udupa +9 more
- 11 Dec 2013
TL;DR: This work describes three different instantiations of the counter-example-guided-inductive-synthesis (CEGIS) strategy for solving the synthesis problem, reports on prototype implementations, and presents experimental results on an initial set of benchmarks.
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