On the complexity of undecidable problems in automata theory

Complementation of finite automata on infinite words is not only a fundamental problem in automata theory, but also serves as a cornerstone for solving numerous decision problems in mathematical logic, model-checking, program analysis and verification. For Streett complementation, a significant gap exists between the current lower bound 2^{Omega(n*log(n*k))} and upper bound 2^{O(n*k*log(n*k))}, where n is the state size, k is the number of Streett pairs, and k can be as large as 2^{n}. Determining the complexity of Streett complementation has been an open question since the late 80's. In this paper we show a complementation construction with upper bound 2^{O(n*log(n)+n*k*log(k))} for k=O(n) and 2^{O(n^{2}*log(n))} for k=Omega(n), which matches well the lower bound obtained in the paper arXiv:1102.2963. We also obtain a tight upper bound 2^{O(n*log(n))} for parity complementation.

Mutation analysis is a popular test assessment method. It relies on the mutation score, which indicates how many mutants are revealed by a test suite. Yet, there are mutants whose behaviour is equivalent to the original system, wasting analysis resources and preventing the satisfaction of the full (100%) mutation score. For finite behavioural models, the Equivalent Mutant Problem (EMP) can be addressed through language equivalence of non-deterministic finite automata, which is a well-studied, yet computationally expensive, problem in automata theory. In this paper, we report on our preliminary assessment of a state-of-the-art exact language equivalence tool to handle the EMP against 3 models of size up to 15,000 states on 1170 mutants. We introduce random and mutation-biased simulation heuristics as baselines for comparison. Results show that the exact approach is often more than ten times faster in the weak mutation scenario. For strong mutation, our biased simulations are faster for models larger than 300 states. They can be up to 1,000 times faster while limiting the error of misclassifying non-equivalent mutants as equivalent to 10% on average. We therefore conclude that the approaches can be combined for improved efficiency.

The use of lists in the study of undecidable problemsin automata theory

There are many decision problems in automata theory (including membership, emptiness, emptiness of intersection, inclusion and universality problems) that for some classes of tree automata are NP-hard. The study of their parameterized complexity allows us to find new bounds of their non-polynomial time algorithmic behaviors. We present results of such a study for classical tree automata (TA), rigid tree automata (RTA), tree automata with global equality and disequality (TAGED) and t-DAG automata.KeywordsModel CheckParameterized ComplexityLinear Temporal LogicFinite AutomatonGlobal ConstraintThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Bideterministic automata are deterministic automata with the property of their reversal automata also being deterministic. It has been known that a bideterministic automaton is the minimal deterministic automaton accepting its language. This paper shows that any bideterministic automaton is the unique minimal automaton among all (including nondeterministic) automata accepting the same language. We also present a more general result that shows that under certain conditions a minimal deterministic automaton accepting some language or the reversal of the minimal deterministic automaton of the reversal language is a minimal automaton representation of the language. These conditions can be checked in polynomial time.