IGETA – Identification and Generation of Early Test Artifacts from Natural Language Requirements

Anurag Dwarakanath, Shubhashis Sengupta, and Roshni R. Ramnani


NLP, functional testing, test intent generation


Most software projects have requirements expressed in natural language. Tools have attempted to automatically generate design models and test scenarios from these requirements with varying degrees of success. In this paper, we present, IGETA, a novel approach to generate early test artifacts from business requirements stated in single sentences. IGETA answers whether a requirement is un-ambiguously testable; what would be the test intents (what functional / non-functional aspects does the requirement mandate); what category does the requirement belong to; and what are the test data in the requirement. We achieve this by leveraging natural language processing technique of dependency parsing associated with a set of pattern matching rules and actions. We describe our approach with examples and discuss some early results

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