A pure weight-based test case generator for context-free grammars is introduced, where weights are set for each rule and used as unique driver to identify which one to apply during the derivation process; we demonstrate why static flat weights generally cannot work whereas, introducing a simple penalty mechanism, a dynamic weight-based approach is always able to produce random but finite sentences; then, looking at the source grammar from an original perspective, we proceed towards the definition of constraints through which, while still guaranteeing the convergence of the derivation, static but balanced weights can be identified: balanced weights own the interesting capability to drive the generation of an inexhaustible number of extremely random, structurally complex but finite sentences by an automatic test case generator having a dramatically simple derivation logic. Thereafter, we illustrate how to design a weight-based system which, by exploiting the characteristics of balanced weights, can be easily configured to produce sentences that address any specific coverage metric, including those not having a definite generative strategy yet. A complete example of the application of the balanced concept and the weight-based system is then included and experimental results are eventually shown.

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