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Register a new userTarTar: A Timed Automata Repair Tool
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Added by
Martin Koelbl
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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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 are guaranteed to eliminate executability of the given TDT, 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.
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Development of software is an iterative process. Graphical tools to represent the relevant entities and processes can be helpful. In particular, automata capture well the intended execution flow of applications, and are thus behind many formal approaches, namely behavioral types. Typestate-oriented programming allow us to model and validate the intended protocol of applications, not only providing a top-down approach to the development of software, but also coping well with compositional development. Moreover, it provides important static guarantees like protocol fidelity and some forms of progress. Mungo is a front-end tool for Java that associates a typestate describing the valid orders of method calls to each class, and statically checks that the code of all classes follows the prescribed order of method calls. To assist programming with Mungo, as typestates are textual descriptions that are terms of an elaborate grammar, we developed a tool that bidirectionally converts typestates into an adequate form of automata, providing on one direction a visualization of the underlying protocol specified by the typestate, and on the reverse direction a way to get a syntactically correct typestate from the more intuitive automata representation.
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Model learning has gained increasing interest in recent years. It derives behavioural models from test data of black-box systems. The main advantage offered by such techniques is that they enable model-based analysis without access to the internals of a system. Applications range from fully automated testing over model checking to system understanding. Current work focuses on learning variations of finite state machines. However, most techniques consider discrete time. In this paper, we present a method for learning timed automata, finite state machines extended with real-valued clocks. The learning method generates a model consistent with a set of timed traces collected by testing. This generation is based on genetic programming, a search-based technique for automatic program creation. We evaluate our approach on 44 timed systems, comprising four systems from the literature and 40 randomly generated examples.
Active learning of timed languages is concerned with the inference of timed automata from observed timed words. The agent can query for the membership of words in the target language, or propose a candidate model and verify its equivalence to the target. The major difficulty of this framework is the inference of clock resets, central to the dynamics of timed automata, but not directly observable. Interesting first steps have already been made by restricting to the subclass of event-recording automata, where clock resets are tied to observations. In order to advance towards learning of general timed automata, we generalize this method to a new class, called reset-free event-recording automata, where some transitions may reset no clocks. This offers the same challenges as generic timed automata while keeping the simpler framework of event-recording automata for the sake of readability. Central to our contribution is the notion of invalidity, and the algorithm and data structures to deal with it, allowing on-the-fly detection and pruning of reset hypotheses that contradict observations, a key to any efficient active-learning procedure for generic timed automata.
This paper offers a survey of uppaalsmc, a major extension of the real-time verification tool uppaal. uppaalsmc allows for the efficient analysis of performance properties of networks of priced timed automata under a natural stochastic semantics. In particular, uppaalsmc relies on a series of extensions of the statistical model checking approach generalized to handle real-time systems and estimate undecidable problems. uppaalsmc comes together with a friendly user interface that allows a user to specify complex problems in an efficient manner as well as to get feedback in the form of probability distributions and compare probabilities to analyze performance aspects of systems. The focus of the survey is on the evolution of the tool - including modeling and specification formalisms as well as techniques applied - together with applications of the tool to case studies.
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