## 2 Background and Related Work
### Artificial-Intelligence-Supported Requirements Elicitation  
Recently, many researchers have conducted studies applying artificial intelligence methodologies in requirements elicitation [8; 9; 10; 11; 12; 13; 14; 15; 16]. Various methods for automated requirements elicitation are applied in the literature and often include terms such as deep learning [17; 18], sentiment analysis [19; 20], or automatic text classification [21; 22; 23]. Artificial intelligence offers the advantage of efficiently analyzing large amounts of data. However, automatically extracting requirements from text data, such as online reviews, still requires human input. Moreover, [24; 25] suggest human reviews of the results of automated requirements elicitation to meet quality expectations and formulate adequate requirements. In 2011, [PERSON] and [PERSON] [16] proposed a method facilitating text mining to support the identification and visualization of product attributes and attribute dimensions among market segments from online camera reviews. By comparing online consumer reviews with expert reviews, the authors showed that consumers and experts value specific product attributes differently. In addition, they determined that segments of users use distinct vocabulary to describe the same features. [PERSON] et al. [12] showed an approach to derive requirements from online reviews using automated text analysis and user-satisfaction analysis. They highlighted the challenge of deriving constructive feedback from user reviews due to complex and diverse opinions. To overcome this challenge, the researchers first adopted opinion-mining techniques such as the'syntactic relation-based propagation approach' [26] to automatically extract opinion expressions and then perform clustering of the terms using macro network topology. Finally, they combined user satisfaction analysis with inherent economic attributes. [PERSON] et al. [8] introduced an artificial-intelligence-based chatbot to interact with users to elicit requirements. By integrating a knowledge base that provides domain-specific requirements from various industries, the chatbot can ask questions to formulate adequate requirements. Finally, the captured requirements were classified into functional and non-functional requirements using machine-learning algorithms such as support vector machine and multinomial naive Bayes. According to the research results, the classification achieved 88% and 91% accuracy, respectively. Other researchers, such as [10; 11; 14; 24; 25], propose using the latent Dirichlet allocation (LDA) algorithm [27; 28] to extract topics from online reviews automatically. In the studies, the researchers conducted sentiment analysis to determine user satisfaction to classify the requirements into bugs and features. In addition to using LDA and sentiment analysis, [PERSON] et al. [15] utilize an adapted version of the Kano model [29] to categorize the customer requirements based on the determined dissatisfaction-satisfaction pair from the sentiment analysis. [PERSON] et al. [11] combined LDA with word-embedding [30] algorithms, such as word2 vec [31] and word mover's distance [32], to summarize requirements automatically. While [PERSON] et al. [33] achieved state-of-the-art results with traditional methods of classifying new requirements based on online reviews, [PERSON] et al. [13] showed that the use of modern transformer algorithms such as BERT [34] can reach up to 87% accuracy when taking over manual classification tasks while drastically reducing the time and costs of evaluation. [PERSON] et al. [35] proposed, with ReqGen, an approach to automatically generate natural language requirements specifications based on specific given keywords using transformer technology. The method consists of multiple steps, such as domain fine-tuning of the language model, ensuring the occurrence of essential keywords in the generated requirement statements, and requirement-syntax-constrained decoding to close semantic and syntax distances.