Tell Me What You See: On a Proposal for a Computer Vision and Sentiment Analysis Tool to Support User Feedback Collection in Software Development
Resumo
O desenvolvimento ágil de software e o Design Centrado no Usuário enfatizam o alinhamento com as necessidades dos usuários e a priorização da coleta eficaz de feedback. Métodos tradicionais de feedback, como entrevistas e questionários, muitas vezes não capturam as reações dos usuários. Para resolver isso, introduzimos o CV4FeC, uma ferramenta que integra o OpenCV para analisar expressões faciais e sentimentos, oferecendo uma abordagem abrangente para entender a comunicação dos usuários. O CV4FeC combina visão computacional e análise de sentimentos para fornecer insights valiosos sobre as reações dos usuários. Uma avaliação preliminar demonstrou sua eficácia em aprimorar a coleta de feedback no desenvolvimento de software.
Palavras-chave:
Software Development, Feedback, Data Collection, Computer Vision
Referências
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Minu, M., Arun, K., Tiwari, A., and Rampuria, P. (2020). Face recognition system based on haar cascade classifier. International Journal of Advanced Science and Technology, 29(5):3799–3805.
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Obaidi, M., Nagel, L., Specht, A., and Klünder, J. (2022). Sentiment Analysis Tools in Software Engineering: A Systematic Mapping Study. Information and Software Technology, 151:107018.
Parizi, R., Prestes, M., Marczak, S., and Conte, T. (2022). How has Design Thinking being Used and Integrated into Software Development Activities? A Systematic Mapping. Journal of Systems and Software, 187:1–27.
Runeson, P., Engström, E., and Storey, M.-A. (2020). The Design Science Paradigm as a Frame for Empirical Software Engineering, pages 127–147. Springer.
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Sharma, A., Pathak, J., Prakash, M., and Singh, J. (2021). Object Detection using Opencv and Python. In Proceedings of the International Conference on Advances in Computing, Communication Control and Networking, pages 501–505. IEEE.
Vázquez-Ingelmo, A., García-Holgado, A., and García-Peñalvo, F. J. (2020). C4 model in a software engineering subject to ease the comprehension of uml and the software. In 2020 IEEE Global Engineering Education Conference (EDUCON), pages 919–924.
Wiley, V. and Lucas, T. (2018). Computer vision and image processing: a paper review. International Journal of Artificial Intelligence Research, 2(1):29–36.
Zhang, J., Li, Y., Tian, J., and Li, T. (2018). Lstm-cnn hybrid model for text classification. In 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pages 1675–1680. IEEE.
Bekhit, A. F. and Bekhit, A. F. (2022). Introduction to Computer Vision. Computer Vision and Augmented Reality in iOS: OpenCV and ARKit Applications, pages 1–20.
Blattgerste, J., Behrends, J., and Pfeiffer, T. (2022). A Web-Based Analysis Toolkit for the System Usability Scale. In Proceedings of the International Conference on PErvasive Technologies Related to Assistive Environments, pages 1–10, Corfu, Greece. ACM.
Brooke, J. (1996). SUS-A Quick and Dirty Usability Scale. Usability Evaluation in Industry, 189(194):4–7.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3):319–340.
Hehn, J., Mendez, D., Uebernickel, F., Brenner, W., and Broy, M. (2020). On Integrating Design Thinking for Human-Centered Requirements Engineering. IEEE Software, 37(2):25–31.
Karolita, D., Grundy, J., Kanij, T., Obie, H., and McIntosh, J. (2023). What’s in a persona? a preliminary taxonomy from persona use in requirements engineering. In International Conference on Evaluation of Novel Approaches to Software Engineering 2023, pages 39–51. Scitepress.
Khan, M., Chakraborty, S., Astya, R., and Khepra, S. (2019). Face Detection and Recognition using OpenCV. In Proceedings of the International Conference on Computing, Communication, and Intelligent Systems, pages 116–119. IEEE.
Li, Z. S., Arony, N. N., Devathasan, K., Sihag, M., Ernst, N., and Damian, D. (2023). Unveiling the life cycle of user feedback: Best practices from software practitioners.
Lin, B., Zampetti, F., Bavota, G., Di Penta, M., Lanza, M., and Oliveto, R. (2018). Sentiment Analysis for Software Engineering: How Far Can We Go? In Proceedings of the International Conference on Software Engineering, ICSE ’18, page 94–104, New York, NY, USA. ACM.
Malviya, S., Tiwari, A. K., Srivastava, R., and Tiwari, V. (2020). Machine learning techniques for sentiment analysis: A review. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 12(02):72–78.
Mehta, K. and Sood, V. M. (2023). Agile software development in the digital world–trends and challenges. Agile Software Development: Trends, Challenges and Applications, pages 1–22.
Minu, M., Arun, K., Tiwari, A., and Rampuria, P. (2020). Face recognition system based on haar cascade classifier. International Journal of Advanced Science and Technology, 29(5):3799–3805.
Obaidi, M. and Klünder, J. (2021). Development and Application of Sentiment Analysis Tools in Software Engineering: A Systematic Literature Review. In Proceedings of the International Conference on Evaluation and Assessment in Software Engineering, EASE ’21, page 80–89, New York, NY, USA. ACM.
Obaidi, M., Nagel, L., Specht, A., and Klünder, J. (2022). Sentiment Analysis Tools in Software Engineering: A Systematic Mapping Study. Information and Software Technology, 151:107018.
Parizi, R., Prestes, M., Marczak, S., and Conte, T. (2022). How has Design Thinking being Used and Integrated into Software Development Activities? A Systematic Mapping. Journal of Systems and Software, 187:1–27.
Runeson, P., Engström, E., and Storey, M.-A. (2020). The Design Science Paradigm as a Frame for Empirical Software Engineering, pages 127–147. Springer.
Sambare, M. (2023). Fer2013 facial expression recognition dataset. Kaggle. Acessado em Dezembro de 2023.
Sang, D. V., Van Dat, N., et al. (2017). Facial expression recognition using deep convolutional neural networks. In 2017 9th International Conference on Knowledge and Systems Engineering (KSE), pages 130–135. IEEE.
Shania, M., Raharjo, T., and Fitriani, A. N. (2023). Implementation user-centered design in agile software development: Systematic literature review. Indonesian Journal of Multidisciplinary Science, 2(7):2812–2831.
Sharma, A., Pathak, J., Prakash, M., and Singh, J. (2021). Object Detection using Opencv and Python. In Proceedings of the International Conference on Advances in Computing, Communication Control and Networking, pages 501–505. IEEE.
Vázquez-Ingelmo, A., García-Holgado, A., and García-Peñalvo, F. J. (2020). C4 model in a software engineering subject to ease the comprehension of uml and the software. In 2020 IEEE Global Engineering Education Conference (EDUCON), pages 919–924.
Wiley, V. and Lucas, T. (2018). Computer vision and image processing: a paper review. International Journal of Artificial Intelligence Research, 2(1):29–36.
Zhang, J., Li, Y., Tian, J., and Li, T. (2018). Lstm-cnn hybrid model for text classification. In 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pages 1675–1680. IEEE.
Publicado
11/11/2024
Como Citar
SOARES, Regis; MESSA, Leonardo; KLEINPAUL, Enzo; PARIZI, Rafael.
Tell Me What You See: On a Proposal for a Computer Vision and Sentiment Analysis Tool to Support User Feedback Collection in Software Development. In: ESCOLA REGIONAL DE ENGENHARIA DE SOFTWARE (ERES), 8. , 2024, Santiago/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 41-50.
DOI: https://doi.org/10.5753/eres.2024.4275.
