Abstract

Crowd-based and data-intensive requirements engineering (RE) strategy is considered a promising approach. It enables the gathering and analyzing of information from the general public or the so-called crowd in order to derive validated user requirements. This study aims to conceptualize the process of analyzing information from a crowd to achieve the fulfillment of user requirements. The created model is based on the ADO framework (Antecedents-Decisions-Outcomes). In the empirical part, we chose the Instagram mobile app and user feedback on it as a source of data for the validation of our approach. For extracting antecedents from user feedback, we applied the Latent Dirichlet Allocation (LDA), and then sentiment analysis was performed for each topic to prioritize the most urgent tasks delegated by the crowd.

Recommended Citation

Baj-Rogowska, A. (2024). The Crowd as a Source of Knowledge - From User Feedback to Fulfilling Requirements. In B. Marcinkowski, A. Przybylek, A. Jarzębowicz, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era (ISD2024 Proceedings). Gdańsk, Poland: University of Gdańsk. ISBN: 978-83-972632-0-8. https://doi.org/10.62036/ISD.2024.109

Paper Type

Full Paper

DOI

10.62036/ISD.2024.109

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The Crowd as a Source of Knowledge - From User Feedback to Fulfilling Requirements

Crowd-based and data-intensive requirements engineering (RE) strategy is considered a promising approach. It enables the gathering and analyzing of information from the general public or the so-called crowd in order to derive validated user requirements. This study aims to conceptualize the process of analyzing information from a crowd to achieve the fulfillment of user requirements. The created model is based on the ADO framework (Antecedents-Decisions-Outcomes). In the empirical part, we chose the Instagram mobile app and user feedback on it as a source of data for the validation of our approach. For extracting antecedents from user feedback, we applied the Latent Dirichlet Allocation (LDA), and then sentiment analysis was performed for each topic to prioritize the most urgent tasks delegated by the crowd.