ABSTRACT
As the number of mobile devices has increased, software development teams have focused on releasing mobile applications, allowing users to carry out transactions, access information and improve their lifestyle more efficiently. Nevertheless, even when providing useful means for carrying out daily tasks, users report dissatisfaction or frustration when using these applications. For energy companies, mobile applications that fail to provide both usefulness and ease of use may reduce their adoption and an increase in the company’s workload, as users will require company workers to solve problems they could solve on their own. In this paper, we report how we applied exploratory testing and ad-hoc usability inspection to identify improvement opportunities during the development of a mobile application that would allow users to measure their power consumption, supporting social distancing in the context of the COVID-19 pandemic. After identified a set of functional and usability problems, the development team redesigned the application, which was perceived as both useful and easy to use from the point of view of the managers that requested it. Also, we report lessons learned that are useful for practitioners willing to replicate this experience. © 2021, Springer Nature Switzerland AG.
ABSTRACT
To mitigate financial loss and follow the recommended sanitary measures due to the COVID-19 pandemic, self-reading, a method in which a consumer reads and reports his own energy consumption, has been presented as an efficient alternative for power companies. In such context, this work presents a solution for self-reading via chatbot in chatting applications. This solution is under development as part of a research and development (R&D) project. It is integrated with a method based on image processing that automatically reads the energy consumption and recognizes the identification code of a meter for validation purposes. Furthermore, all processes utilize cognitive services from the IBM Watson platform to recognize intentions in the dialog with the consumers. The dataset used to validate the proposed method for self-reading contains examples of analogical and digital meters used by Equatorial Energy group. Preliminary results presented accuracies of 77.20% and 84.30%, respectively, for the recognition of complete reading sequences and identification codes in digital meters and accuracies of 89% and 95.20% in the context of analogical meters. Considering both meter types, the method obtains an accuracy per digit of 97%. The proposed method was also evaluated with UFPR-AMR public dataset and achieves a result comparable to the state of the art. © 2021, Brazilian Society for Automatics--SBA.