Abstract
Machine Learning (ML) has shown great potential for automating several aspects of everyday life and business. Experiences reports on developing ML systems, however, focus mainly on how to install, configure and maintain an ML system, but do not focus on the requirements engineering process and the design of the user interface that will present the data to the user. In this paper, we report the lessons learned from applying different requirements and design methods in one of the stages of a large development project (18 months). Its goal was to develop and evaluate a Web based application embedding an ML model that would produce data on the probability of lawsuits based on features such as: number of complaints, electrical damage to appliances and power shutdowns. At all, the design team applied the following techniques: (a) Interviews and document analysis, to identify the major reasons for filling a lawsuit; (b) Personas, to define which type of clients could file a lawsuit; (c) Scenarios, to define which interaction (conversations) would be automatically triggered by a chatbot to try to solve client problems, avoiding a lawsuit; and (d) Prototype evaluation, to define the interaction and type of data that would be available to the lawyers through a Web application. Through the lessons learned within this paper and by providing details for its replication, we intend to encourage software companies to combine requirements and design approaches for cost-effective user centered design, especially in decision support intelligent systems.
Supported by Equatorial Energia.
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Notes
- 1.
Brazilian Website “Complain Here” (“Reclame Aqui” in Portuguese) - https://www.reclameaqui.com.br/.
- 2.
Equatorial Power Company Brazilian Website - https://ma.equatorialenergia.com.br/.
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Acknowledgments
This work was supported by SijurI project funded by Equatorial Energy under the Brazilian Electricity Regulatory Agency (ANEEL) P&D Program Grant number APLPED00044_PROJETOPED_0036_S01. Additionally, this work was supported by the Foundation for the Support of Research and Scientific Development of Maranh(FAPEMA), the Coordination for the Improvement of Higher Education Personnel (CAPES) and the National Council for Scientific and Technological Development (CNPq).
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Rivero, L. et al. (2021). Lessons Learned from Applying Requirements and Design Techniques in the Development of a Machine Learning System for Predicting Lawsuits Against Power Companies. In: Yamamoto, S., Mori, H. (eds) Human Interface and the Management of Information. Information Presentation and Visualization. HCII 2021. Lecture Notes in Computer Science(), vol 12765. Springer, Cham. https://doi.org/10.1007/978-3-030-78321-1_18
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