Sai Chaithra Allala, Florida International University, USA
Juan Pablo Sotomayor Paez, Florida International University, USA
Dionny Santiago, Florida International University, Ultimate Software, USA
Peter J. Clarke, Florida International University, USA
Testing continues to be the major approach to ensuring the quality of software during development. Although there have been many attempts to automate the generation of test cases from user requirements, the widespread approach to creating test cases still continues to be mainly a manual process. With the advances of Model-driven Software Engineering (MDSE), Artificial Intelligence (AI) and Natural Language Processing (NLP), the possibility of further automating the generation of test cases from requirements is increasing. But how can we exploit these advances in MDSE, AI, and NLP technologies to further automate requirements-based test creation? This poster proposes an approach that uses use a model-to-model (M2M) transformation to convert user requirements into test cases with the support of a knowledge base. A prototype was created that validates the user requirements (use cases and user stories) with a meta-model and performs initial NLP processing for the extraction of grammatical relationships from the user requirements. The preliminary study involves the analysis of 50 use cases from student projects for 5 applications; and 50 user stories from an industrial application.
- Investigating the feasibility of automatically generating test cases from requirements.
- Leveraging MDSE, AI and NLP for improving the efficiency of software testing.
- Transforming models to models and validating the results.
- Bridging gaps between academic research and industry practices.