Ensuring quality of requirements thanks to AI

Compiling all the requirements for software, for example, is a complex task. Above all, achieving a high and uniform quality of the requirements description is a challenge. Artificial Intelligence can help to check and thus ensure the quality of requirements.

For this purpose, AI evaluates the requirements in terms of their quality based on defined and learned rules. For those requirements that fall below a defined level of quality, the violated rules are displayed. Depending on the quality criteria, companies can use AI methods from the fields of Machine Learning or Natural Language Processing (NLP). Natural Language Processing captures natural language and processes it, thanks to specific rules and algorithms. The semantics and grammatical structures of the language are examined.

Companies can check the completeness of requirements using the Part of Speech Tagging (POS tagging) method. Part of speech Tagging means assigning words and punctuation marks in a text to part of speech. Subsequently, the testability and comprehensibility of requirements can be checked using ML methods such as Text Classification. Text Classification is a machine learning technique that assigns a set of predefined categories to an open text. Through the method, pretty much any kind of text can be organized, structured and categorized.

This is what companies need to consider

For companies, especially the development of the solution system is time-consuming. The demands on the requirements quality depend on the respective company. The development of the competence of the employees is also not to be neglected, since the employees must be able to process the marked requirements on the basis of the recommendations of the system.

If the requirements quality is checked and ensured with the help of artificial intelligence, there is a very high benefit for the direct improvement of the end product. In addition, companies can improve their process performance. They can streamline their requirements process in terms of review and correction loops and also increase data quality.