User Personas Improve Social Sustainability by Encouraging Software Developers to Deprioritize Antisocial Features

Background: Sustainable software development involves creating software in a manner that meets present goals without undermining our ability to meet future goals. In a software engineering context, sustainability has at least four dimensions: ecological, economic, social, and technical. No interventions for improving social sustainability in software engineering have been tested in rigorous lab-based experiments, and little evidence-based guidance is available. Objective: The purpose of this study is to evaluate the effectiveness of two interventions—stakeholder maps and persona models—for improving social sustainability through software feature prioritization. Method: We conducted a randomized controlled factorial experiment with 79 undergraduate computer science students. Participants were randomly assigned to one of four groups and asked to prioritize a backlog of prosocial, neutral, and antisocial user stories for a shopping mall's digital screen display and facial recognition software. Participants received either persona models, a stakeholder map, both, or neither. We compared the differences in prioritization levels assigned to prosocial and antisocial user stories using Cumulative Link Mixed Model regression. Results: Participants who received persona models gave significantly lower priorities to antisocial user stories but no significant difference was evident for prosocial user stories. The effects of the stake-holder map were not significant. The interaction effects were not significant. Conclusion: Providing aspiring software professionals with well-crafted persona models causes them to de-prioritize antisocial software features. The impact of persona modelling on sustainable software development therefore warrants further study with more experience professionals. Moreover, the novel methodological strategy of assessing social sustainability behavior through backlog prioritization appears feasible in lab-based settings.