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Learning & Testing Meetings

Every friday at 11:00 in room M1.0.02 a meeting is held in which ongoing work in the area of automata learning and testing is discussed. Also from time to time a small informal presentation over a specific topic is held with some discussion afterwards.

Upcoming talks

  • Omar Duhaiby
    June 16th
    Learning an industrial software driver using LearnLib

Omar is a PhD student at the TU/e, applying learning at various companies.

Past talks

  • SUMBAT meeting
    June 9th
    Learning an industrial software driver using LearnLib

the SUMBAT project (SUperSizing Model-BAsed Testing) will organize a small workshop, presenting first project results.

10:05 Model-Based Testing with TorXakis - Pierre van de Laar (TNO-ESI)

10:30 Model-Based Testing at Oce - Ramon Janssen (RU)

11:10 Model Learning at PANalytical - Jeroen Meijer (UT)

11:35 Model-Based Testing with Complete Test Suites - Petra van den Bos (RU)

  • Alexis Linard
    October 3rd, 14:00 October 4th, 15:00, this is a Tuesday!
    Towards Adaptive Scheduling of Maintenance for Cyber-Physical Systems

Alexis will crash test his presentation for ISoLA conference.

  • Tanja Vos
    September 30th
    Test*

Tanja Vos will present her testing tool Test*.

  • Petra van den Bos
    August 19th
    Small example and tool demo of ADS algorithm for IOTSes
  • Paul Fiterau
    July 15th
    Tomte determinizer: move to symbolism?

Paul will wish you a very nice holiday, but before that, he will touch on the key aspect of the determinizer in Tomte, how it actually works in the tool, limitations and discuss a move to a more symbolic framework.

  • Ramon Janssen
    July 1st
    Combining Model Learning and Model Checking to Analyze TCP Implementations

Ramon will practice his talk at CAV about learning TCP with abstraction, and model checking with concretization of the abstract models.

  • Jan Friso Groote
    May 27th June 3rd
    An O(m log n) Algorithm for Stuttering Equivalence and Branching Bisimulation

In 1989 Rob van Glabbeek and Peter Weijland defined branching bisimulation as an alternative to weak bisimulation of Robin Milner. Frits Vaandrager and I formulated an algorithm with complexity O(mn) where m is the number of transitions of a labelled transition system and n is the number of states. This was quite a spectacular improvement over weak bisimulation which is cubic due to the transitive tau-closure. At that time there was also the O(mn) algorithm for strong bisimulation, by Kannelakis and Smolka, which was spectacularly improved by Paige and Tarjan to O(mlog n). To me it was open whether the algorithm for branching bisimulation could be improved, until a discussion with Anton Wijs about implementing the algorithms on a GPU, brought me to investigate the paper of Paige and Tarjan in relationship to the branching bisimulation algorithm again. This led to the insight to obtain the current algorithm (except for a small and easily repairable flaw, pointed out by Jansen and Keiren). The algorithm is amazingly complex, but it outperforms existing algorithms by orders of magnitude, especially if systems become large.

A preprint of the paper can be found at http://arxiv.org/abs/1601.01478

  • Petra van den Bos
    May 27th
    Enhancing Automata Learning by Log-Based Metrics

Petra will practice her talk for iFM 2016

  • MariŽlle Stoelinga
    April 15th Wednesday May 25th 11:00 HG00.086
    Distances on labeled transition systems

In this talk, I will extend the basic system relations of trace inclusion, trace equivalence, simulation, and bisimulation to a quantitative setting in which propositions are interpreted not as boolean values, but as real values in the interval [0,1]. Trace inclusion and equivalence give rise to asymmetrical and symmetrical linear distances, while simulation and bisimulation give rise to asymmetrical and symmetrical branching distances. I will present the relationships among these distances, and we provide a full logical characterization of the distances in terms of quantitative versions of LTL and μ-calculus. I will show that, while trace inclusion (resp. equivalence) coincides with simulation (resp. bisimulation) for deterministic boolean transition systems, linear and branching distances do not coincide for deterministic quantitative transition systems. Finally, I will go into algorithms for computing the distances.

(This work dates back to when I was at UC Santa Cruz and is joint work with Luca de Alfaro and Marco Faella)

  • Mark Janssen
    April 8th
    [Combining active learning with fuzzing]
  • Petra van den Bos
    March 18th
    TBA

trial presentation ICT.OPEN

  • Rick Smetsers
    March 11th
    Separating sequences for all pairs of states

Rick will practice his talk for LATA 2016 about finding minimal separating sequences for all pairs of state in O(n log n).

  • Jan Tretmans
    March 4th
    Workshop TorXakis
  • David N. Jansen
    January 29th
    An O(m log n) Algorithm for Stuttering Equivalence and Branching Bisimulation

David will talk about a paper by Jan Friso Groote Anton Wijs: http://arxiv.org/abs/1601.01478

  • Alexander Fedotov
    December 11th
    The hybrid UIO method

I will report on my research internship.

  • Joshua Moerman
    November 27th
    The different learning algorithms for register automata

I will discuss the differences between the various learning algorithms for register automata. This includes the abstract learning algorithm from nominal automata, our tool Tomte and RAlib from Uppsala (and maybe more).

  • Petra van den Bos
    November 6th
    Adaptive distinguishing sequences for deterministic IOTSes

As far as I know, there only exist random test methods to generate test cases from an LTS/IOTS-model. I will discuss an adapted version of the FSM-based Lee and Yannakakis-method to find adaptive distinguishing sequences. These sequences enable a smarter and more directed way of testing.

  • Alexis Linard
    October 30th
    Preliminary work on Printhead Failure Prediction

As part of the Cyber-Physical Systems project in partnership with Ocť, my current goal is to use Machine Learning (and other) techniques in order to predict printers failures. I will present here not only the current achievements or Machine Learning techniques employed, but also the main challenges to be completed. My presentation will also trigger a debate on the multidisciplinarity of the project.

  • Paul Fiterau-Brostean
    October 23th
    Learning Register Automata with Fresh Value Generation

I will give a brief overview on our CEGAR based algorithm and detail on how we have extended this algorithm to handle fresh output values. I will also talk on some of the optimizations we've made to the algorithm, and to the tool implementing it. The presentation is going to be a draft version of what I am going to present in Colombia next week.