Compare requirements documents automatically

If you want to compare requirements documents in the company, such as feedback from suppliers, this can be very time-consuming. Artificial Intelligence methods can help. We will show you which methods can be of assistance to you.

The first step is to interpret the data. This is done with the help of Natural Language Processing (NLP) or Text Mining. With Natural Language Processing, natural language can be captured and processed in a computer-based way thanks to rules and algorithms. The semantics and grammatical structures of the language are examined. In contrast, text mining does not take semantic features into account. For this, the method is particularly useful in the analysis of unstructured text data. For example, automatic analysis can be used to extract key statements from texts without having to read the texts themselves.

In this case, the text mining method Tokenization helps us to decompose the requirement documents into individual parts. Tokenization is a common task in natural language processing (NLP). In this method, a text is broken down into smaller units called tokens. Tokens can be either words, characters or subwords.

Once the text has been decomposed, comparison can take place by using Information Retrieval or sentence analysis, i.e. methods from text mining or NLP. Information retrieval can be used to analyze requirements for existing conflict. Information retrieval involves the evaluation of unstructured data, as search engines do with the Internet, for example.

This is what companies need to consider

For companies, the effort lies primarily in the creation of the solution systematics, since it must be precisely defined for Artificial Intelligence what counts as a difference in the documents to be compared and how these are further treated. In addition, access to the APIs of the tools in which the requirements documents are located must be ensured.

Automated comparison of requirements can save companies a lot of time and directly improve the end product. This also increases the quality of the data and documents.

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