Hing Leung

Adaptive random testing

In this paper, we introduce an enhanced form of random testing called Adaptive Random Testing. Adaptive random testing seeks to distribute test cases more evenly within the input space. It is based on the intuition that for non-point types of failure patterns, an even spread of test cases is more likely to detect failures using fewer test cases than ordinary random testing. Experiments are performed using published programs. Results show that adaptive random testing does outperform ordinary random testing significantly (by up to as much as 50%) for the set of programs under study. These results are very encouraging, providing evidences that our intuition is likely to be useful in improving the effectiveness of random testing.

On the relation between ambiguity and nondeterminism in finite automata

Abstract Nondeterminism in a finite automaton is measured dynamically by counting the number of guesses that the automaton has to make in order to recognize an input string. When the amount of nondeterminism is small (bounded) or large (linear in the input length), nothing can be concluded about the amount of ambiguity in the automaton. But when the amount of nondeterminism is intermediate between these extremes, the degree of ambiguity must be infinite.

Limitedness theorem on finite automata with distance functions: an algebraic proof

A distance function on a finite automaton M is defined by assigning to each transition a distance of nonnegative integer value. M is said to be limited in distance if there is a nonnegative integer k such that, for each accepted string w in the language of M, there is an accepting path p for w on which the sum of distances is bounded by k. The limitedness theorem [6,9] on finite automata with distance functions states that it is decidable if an arbitrary finite automaton with a distance function is limited in distance. In this paper, we give an algebraic proof of the theorem and derive from it an exponential-time decision algorithm. In addition, we prove that the decision problem is PSPACE-hard.

The limitedness problem on distance automata: Hashiguchi's method revisited

Hashiguchi has studied the limitedness problem of distance automata (DA) in a series of paper [(J. Comput. System Sci. 24 (1982) 233; Theoret. Comput. Sci. 72 (1990) 27; Theoret. Comput. Sci. 233 (2000) 19)]. The distance of a DA can be limited or unbounded. Given that the distance of a DA is limited, Hashiguchi has proved in Hashiguchi (2000) that the distance of the automaton is bounded by 24n3+n lg(n+2)+n, where n is the number of states. In this paper, we study again Hashiguchis solution to the limitedness problem. We have made a number of simplification and improvement on Hashiguchis method. We are able to improve the upper bound to 23n3+n lg n+n-1.

On Finite Automata with Limited Nondeterminism

Abstract. We develop a new algorithm for determining if a given nondeterministic finite automaton is limited in nondeterminism. From this, we show that the number of nondeterministic moves of a finite automaton, if limited, is bounded by

On factorization forests of finite height

2^{n} - 2

Tight Lower Bounds on the Size of Sweeping Automata

where

Test case selection with and without replacement

n

On the size of parsers and LR( k )-grammars

is the number of states. If the finite automaton is over a one-letter alphabet, using Gohons result the number of nondeterministic moves, if limited, is less than

Measuring Nondeterminism in Pushdown Automata

n^{2}