Towards the acceptance of a Prompt Engineering-based Approach to Build Proto-personas during Product Discovery
Resumo
Product discovery approaches such as Lean Inception (LI) typically span five days (40 working hours). During LI, the participants create and refine proto-personas during four working hours to understand user needs. Proto-personas are preliminary representations of ideal users that guide initial design discussions. The accuracy of the generated proto-personas has been counter-intuitive due to the limited time. There is a gap in exploring the use of prompt engineering and proto-persona strategies to support the Product Discovery approaches. We report an exploratory case study where six participants used a prompt engineering-based approach to generate proto-personas in Product Discovery (LI). Participants accepted our approach well. Our approach used an average of 11 minutes of working hours (SD ≈ 2.24 minutes), traditionally this time in LI is four hours.
Referências
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Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9):1–35.
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Trieflinger, S., Lang, D., Spies, S., and Münch, J. (2023). The discovery effort worthiness index: How much product discovery should you do and how can this be integrated into delivery? Information and software technology, 157:107167.
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., and Schmidt, D. C. (2023). A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382.
Zhang, X., Liu, L., Wang, Y., Liu, X., Wang, H., Arora, C., Liu, H., Wang, W., and Hoang, T. (2024). Auto-generated personas: Enhancing user-centered design practices among university students. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, pages 1–7.
Atlas, S. (2023). Chatgpt for higher education and professional development: A guide to conversational ai.
Belzner, L., Gabor, T., and Wirsing, M. (2023). Large language model assisted software engineering: prospects, challenges, and a case study. In International Conference on Bridging the Gap between AI and Reality, pages 355–374. Springer.
Caroli, P. (2017). Lean inception. São Paulo, BR: [link].
Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis. sage.
Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., and Zhang, J. M. (2023). Large language models for software engineering: Survey and open problems. arXiv preprint arXiv:2310.03533.
Gothelf, J. (2013). Lean UX: Applying lean principles to improve user experience. ” O’Reilly Media, Inc.”.
Karolita, D., McIntosh, J., Kanij, T., Grundy, J., and Obie, H. O. (2023). Use of personas in requirements engineering: A systematic mapping study. Information and Software Technology, page 107264.
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., and Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9):1–35.
Losana, P., Castro, J. W., Ferre, X., Villalba-Mora, E., and Acuña, S. T. (2021). A systematic mapping study on integration proposals of the personas technique in agile methodologies. Sensors, 21(18):6298.
Marques, N., Silva, R. R., and Bernardino, J. (2024). Using chatgpt in software requirements engineering: A comprehensive review. Future Internet, 16(6):180.
Rainer, A. and Wohlin, C. (2022). Recruiting credible participants for field studies in software engineering research. Information and Software Technology, 151:107002.
Runeson, P. and Höst, M. (2009). Guidelines for conducting and reporting case study research in software engineering. Empirical software engineering, 14:131–164.
Salminen, J., Wenyun Guan, K., Jung, S.-G., and Jansen, B. (2022). Use cases for design personas: A systematic review and new frontiers. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pages 1–21.
Trieflinger, S., Lang, D., Spies, S., and Münch, J. (2023). The discovery effort worthiness index: How much product discovery should you do and how can this be integrated into delivery? Information and software technology, 157:107167.
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., and Schmidt, D. C. (2023). A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382.
Zhang, X., Liu, L., Wang, Y., Liu, X., Wang, H., Arora, C., Liu, H., Wang, W., and Hoang, T. (2024). Auto-generated personas: Enhancing user-centered design practices among university students. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, pages 1–7.
Publicado
30/09/2024
Como Citar
AYACH, Fernando Ribeiro; FONTÃO, Awdren.
Towards the acceptance of a Prompt Engineering-based Approach to Build Proto-personas during Product Discovery. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - CONGRESSO BRASILEIRO DE SOFTWARE: TEORIA E PRÁTICA (CBSOFT), 15. , 2024, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 49-58.
DOI: https://doi.org/10.5753/cbsoft_estendido.2024.4096.
