Introducing a Pure Weight-Based Concept for Automatic Test Case Generators

For decades, automatic test case generators for context-free grammars usually have adopted a derivation method designed to obtain the minimum set of shortest sentences.<div>Within this paper, a <i>pure weight-based</i> approach is described: such approach uniquely uses <i>weights </i>to select a rule while expanding a nonterminal symbol; we will assess how it is always possible to identify <i>balanced </i>weights which ensure the convergence of the process and we will see how the generated sentences are usually more complex than the shortest ones, so having more chances to exercise the underlying system more extensively.</div>

Alternative Vidhi to Conversion of Cyclic CNF->GNF

In automata theory Greibach Normal Form shows that A->aV n*, where ‘a’ is terminal symbol and Vn is nonterminal symbol where * shows zero or more rates of Vn [1]. Most popular questions, conversion of following cyclic CNF into GNF are: Question 1 S->AA | a, A->SS | b Question 2 S->AB, A->BS | b, B->SA | a Question 3 S->AB, A->BS | b, B->AS | a [1] To solve these questions, we need two technical lemmas and required one or more another variable like Z1. In these questions, we have cyclic nature of production called cyclic CNF. We have modified the same rule by which we get the more reliable answer with less number of productions in right hand side without using lemmas and any another variable. This above method can be applied on all problems by which we produce the GNF.

CONTEXT-SENSITIVITY OF TWO-DIMENSIONAL REGULAR ARRAY GRAMMARS

Regular array grammars (RAGs) are the lowest subclass in the Chomsky-like hierarchy of isometric array grammars. The left-hand side of each rewriting rule of RAGs has one nonterminal symbol and at most one "#" (a blank symbol). Therefore, the rewriting rules cannot sense contexts of non-# symbols. However, they can sense # as a kind of context. In this paper, we investigate this #-sensing ability. and study the language generating power of RAGs. Making good use of this ability, We show a method for RAGs to sense the contexts of local shapes of a host array in a derivation. Using this method, we give RAGs which generate the sets of all solid upright rectangles and all solid squares. On the other hand. it is proved that there is no context-free array grammar (and thus no RAG) which generates the set of all hollow upright rectangles.