Natural Language (NL) deliverables suffer from ambiguity, poor understandability, incompleteness, and inconsistency. Howewer, NL is straightforward and stakeholders are familiar with it to produce their software requirements documents. This paper presents a methodology, SOLIMVA, which aims at model-based test case generation considering NL requirements deliverables. The methodology is supported by a tool that makes it possible to automatically translate NL requirements into Statechart models. Once the Statecharts are derived, another tool, GTSC, is used to generate the test cases. SOLIMVA uses combinatorial designs to identify scenarios for system and acceptance testing, and it requires that a test designer defines the application domain by means of a dictionary. Within the dictionary there is a Semantic Translation Model in which, among other features, a word sense disambiguation method helps in the translation process. Using as a case study a space application software product, we compared SOLIMVA with a previous manual approach developed by an expert under two aspects: test objectives coverage and characteristics of the Executable Test Cases. In the first aspect, the SOLIMVA methodology not only covered the test objectives associated to the expert's scenarios but also proposed a better strategy with test objectives clearly separated according to the directives of combinatorial designs. The Executable Test Cases derived in accordance with the SOLIMVA methodology not only possessed similar characteristics with the expert's Executable Test Cases but also predicted behaviors that did not exist in the expert's strategy. The key benefits from applying the SOLIMVA methodology/tool within a Verification and Validation process are the ease of use and, at the same time, the support of a formal method consequently leading to a potential acceptance of the methodology in complex software projects.
