The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning

  • Malte Isberner
  • Falk Howar
  • Bernhard Steffen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8734)


In this paper we present TTT, a novel active automata learning algorithm formulated in the Minimally Adequate Teacher (MAT) framework. The distinguishing characteristic of TTT is its redundancy-free organization of observations, which can be exploited to achieve optimal (linear) space complexity. This is thanks to a thorough analysis of counterexamples, extracting and storing only the essential refining information. TTT is therefore particularly well-suited for application in a runtime verification context, where counterexamples (obtained, e.g., via monitoring) may be excessively long: as the execution time of a test sequence typically grows with its length, this would otherwise cause severe performance degradation. We illustrate the impact of TTT’s consequent redundancy-free approach along a number of examples.


Model Check Finite Automaton Symbol Execution Membership Query Observation Table 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Malte Isberner
    • 1
  • Falk Howar
    • 2
  • Bernhard Steffen
    • 1
  1. 1.Dept. of Computer ScienceTU Dortmund UniversityDortmundGermany
  2. 2.Carnegie Mellon UniversityMoffett FieldUSA

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