## 2 Related Study
The study data provided valuable insights on technique characteristics and paved the way to transition to more modern applications. One of these modern applications can be found in the work of [PERSON] et al. [16], who attempted to implement an analytic network process method for selecting the elicitation technique. Furthermore, the study has distinguished the application of the analytical network process in the elicitation technique selection regarding project attributes. Again, [PERSON] et al. [17] provided another model to classify the elicitation technique based on the attributes through the k-nearest neighbor's algorithm implementation. The results were promising, but this success was attributed to the small size of the implemented dataset. Furthermore, the study validated the results using only the similarity rates. Next, [PERSON], [PERSON]. [18] presented requirements conflict resolution and communication model for the telecommunication sector. The model implemented the normalized cross-correlations function (NCCF) to detect requirements conflict. Moreover, the model validated the results using the standard error (SE) function. Although the model was successful, it was based on a statistical assertion. However, the model overlooked the technique selection process. [PERSON] and [PERSON] further attempted to automate the requirement elicitation process [19] by presenting a requirements-collector tool. Although machine learning and deep learning were used to implement the mechanism, much diligence was given to the text classification phase of the process [20; 21] which overlooks the technique selection decision-making process. Alternatively, [PERSON] et al. [22] presented a framework for improved software requirements elicitation. The proposed framework was implemented in a real-life Norway-based IT project. Although, the result of the framework was statistically helpful, a further transition towards automation using machine learning and deep learning was needed. Next, the negative impact of improper requirement selections was investigated by [PERSON] et al. [23]. As a result of the study, the authors presented a partial selection of the software requirements model using a fuzzy method. The experiments revealed enhanced requirement reports that contributed to building the desired software. However, the study needs further investigate the negative influence of the applied method. Henceforth, the research community has shifted its focus towards more robust technologies in search of a solution. Thus, [PERSON]. [24] developed deep learning model in software requirements engineering. The following study presented a transitional state in the type of technology applied to the field. However, the conducted study was implemented on the classification phase of the requirement only. Similarly, [PERSON], [PERSON], and [PERSON]. [25] presented a novel study using a deep neural network to automate the extraction of non-functional requirements. The model is yet another transitional state used in requirement engineering, and demonstrated promising results. However, the proposed model overlooks the elicitation technique selection decision-making process. Conversely, [PERSON] and [PERSON]. [26] thought to assist the requirements process by developing a selection by clustering model. The produced results of the three different experiments were promising. However, the study posed a few threats to the validity related to the experimental methodology. Finally, [PERSON] et al. [27] proposed a requirement conflict detection model using machine learning that reduces the processing overhead through automation sequence operation. Although the results were promising, the study revealed validation threats concerning the size of the conducted experiment. Moreover, the proposed model has tended to focus on requirement conflict detection rather than the selection of technique decision-making process. The literature review was rich with various successful approaches and studies about elicitation technique selection.