UX-RIVIS: Visualization of information about UX data based on app reviews - Campos, Ariel E. S.; Zaina, Luciana (Anais do Simpósio Brasileiro sobre Fatores Humanos em Sistemas Computacionais (IHC), 2024)
Information Visualization (InfoVis) provides techniques and methods for interactive visual representations to enhance human understanding of data. This facilitates monitoring and makes relevant information accessible to all users, regardless of their experience in data analysis. Software professionals in mobile development have shown interest in using visual representations (e.g., charts, tables) to support the analysis of user reviews of applications available in app stores. However, analysis tools often focus on data mining resources and do not provide appropriate visualizations for the target audience (i.e., mobile software professionals). Considering the context previously described, this paper presents the main results of a dissertation project. In this project, a set of 16 guidelines was proposed to support the design of UX data visualizations. The guidelines were conceived from a Systematic Literature Mapping (SLM) that explored 21 papers from academic literature. These papers were analyzed to extract the guidelines in the form of recommendations. The guidelines, along with Shneiderman’s seminal work `Visual Information-Seeking Mantra’, were utilized to develop a proposed UX data visualization named User eXperience Reviews Information Visualization (UX-RIVIS). Shneiderman’s mantra allowed for the introduction of levels of navigation, which produced visualizations ranging from data overview to detailed data. UX-RIVIS visualizations were evaluated by 23 software professionals using the method of Intermediate Semiotic Inspection (i.e., a qualitative approach for assessment). The evaluation provided results that examined the communicability and perceptiveness of software professionals regarding the ease of use of the visualizations. The results indicated the usefulness of the visualizations provided by UX-RIVIS for software professionals in data analysis. It was also noted that these professionals preferred certain visualizations, such as tables and pie charts. Moreover, participants showed interest in using the visualizations for their work activities.