Requirements Engineering

New advances in text analytics make the tech news nearly every week, most prominently IBM Watson, but also more recently AI approaches such as ELMo or BERT. And now it made world news with the pandemic caused by the Covid-19 virus, with the white house requesting help via NLP.

Text Analytics and Natural Language Processing (NLP) deal with all types of automatic processing of texts and is often built on top of machine learning or artificial intelligence approaches. The idea of this article is not to explain how text analytics works, but instead to explain what is possible.

If you ever had quality defects in your requirements-suite or test-suite, you know how time-consuming and expensive they can become. However, due to the sheer size of requirements-suites and test-suites, assessing the quality of the contained artifacts is almost impossible. So, is there no way out of this mess, or do you have to stick deep in this yogurt? There is help: The automated requirements and test analysis tool Scout by Qualicen comes now in a new and improved version!

Requirement documentation is mainly done in either Natural Language (NL) or in formal models like UML or SysML. NL offers the lowest learning curve and the most flexibility, which for many companies means: "Everyone can start writing requirements without formal training". In contrast, formal modelling languages require a considerable effort to learn and are very restrictive. But, the flexibility of NL comes with ambiguity and inconsistency. These are two major downsides that formal modeling languages aim to eliminate. Our customers often ask: "Is there something in the middle, keeping the benefits of NL, but reducing the downsides?" our answer: "Yes, a requirement syntax". But what has that children's puzzle to do with writing requirements?