Martin Kölbl

dblp:221/1706 · DBLP profile ↗
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8ranked-venue papers
7as first author
2since 2021 · last 2022
0000-0002-8959-5332ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 7 · 6 first-author · 1 since 2021Theory of computation · 3 · 3 first-author · 1 since 2021
YearPublicationVenuePosition
2022 Automated Consistency Analysis for Legal Contracts
Alan Khoja, Martin Kölbl, Stefan Leue, Rüdiger Wilhelmi
SPIN2
2021 Automated repair for timed systems
abstract
Abstract We present algorithms and techniques for the repair of timed system models, given as networks of timed automata (NTA). The repair is based on an analysis of timed diagnostic traces (TDTs) that are computed by real-time model checking tools, such as UPPAAL, when they detect the violation of a timed safety property. We present an encoding of TDTs in linear real arithmetic and use the MaxSMT capabilities of the SMT solver Z3 to suggest a minimal number of possible syntactic repairs of the analyzed model. The suggested repairs include modified values for clock bounds in location invariants and transition guards, adding or removing clock resets, etc. We then present an admissibility criterion, called functional equivalence, which ensures that the proposed repair preserves the functional behavior of the considered NTA. We discuss a proof-of-concept tool called TarTar that we have developed, implementing the repair and admissibility analysis, and give insights into its design and architecture. We evaluate the proposed repair technique on faulty mutations generated from a diverse suite of case studies taken from the literature. We show that TarTar can admissibly repair for 69– $$88\%$$ 88 % of the seeded errors in the considered system models.
Martin Kölbl, Stefan Leue, Thomas Wies
Formal Methods Syst. Des.1
2020 TarTar: A Timed Automata Repair Tool
abstract
We present TarTar , an automatic repair analysis tool that, given a timed diagnostic trace (TDT) obtained during the model checking of a timed automaton model, suggests possible syntactic repairs of the analyzed model. The suggested repairs include modified values for clock bounds in location invariants and transition guards, adding or removing clock resets, etc. The proposed repairs guarantee that the given TDT is no longer feasible in the repaired model, while preserving the overall functional behavior of the system. We give insights into the design and architecture of TarTar , and show that it can successfully repair 69% of the seeded errors in system models taken from a diverse suite of case studies.
Martin Kölbl, Stefan Leue, Thomas Wies
CAV (1)1
2020 An Algorithm to Compute a Strict Partial Ordering of Actions in Action Traces
Martin Kölbl, Stefan Leue
ISoLA (4)1
2019 An Efficient Algorithm for Computing Causal Trace Sets in Causality Checking
Martin Kölbl, Stefan Leue
ATVA1
2019 Clock Bound Repair for Timed Systems
abstract
We present algorithms and techniques for the repair of timed system models, given as networks of timed automata (NTA). The repair is based on an analysis of timed diagnostic traces (TDTs) that are computed by real-time model checking tools, such as UPPAAL, when they detect the violation of a timed safety property. We present an encoding of TDTs in linear real arithmetic and use the MaxSMT capabilities of the SMT solver Z3 to compute possible repairs to clock bound values that minimize the necessary changes to the automaton. We then present an admissibility criterion, called functional equivalence, that assesses whether a proposed repair is admissible in the overall context of the NTA. We have implemented a proof-of-concept tool called TarTar for the repair and admissibility analysis. To illustrate the method, we have considered a number of case studies taken from the literature and automatically injected changes to clock bounds to generate faulty mutations. Our technique is able to compute a feasible repair for $$91\%$$ of the faults detected by UPPAAL in the generated mutants.
Martin Kölbl, Stefan Leue, Thomas Wies
CAV (1)1
2018 Automated Functional Safety Analysis of Automated Driving Systems
Martin Kölbl, Stefan Leue
FMICS1
2018 From SysML to Model Checkers via Model Transformation
Martin Kölbl, Stefan Leue, Hargurbir Singh
SPIN1