Towards a Recommender System-based Process for Managing Risks in Scrum Projects

Agile Software Development (ASD) implicitly manages risks through, for example, its short development cycles (i.e., iterations). The absence of explicit risk management activities in ASD might be problematic since this approach cannot handle all types of risks, might cause risks (e.g., technical debt), and does not promote knowledge reuse throughout an organization. Thus, there is a need to bring discipline to agile risk management. This study focuses on bringing such discipline to organizations that conduct multiple projects to develop software products using ASD, specifically, the Scrum framework, which is the most popular way of adopting ASD. For this purpose, we developed a novel solution that was articulated in partnership with an industry partner. It is a process to complement the Scrum framework to use a recommender system that recommends risks and response plans for a target project, given the risks registered for similar projects in an organization's risk memory (i.e., database). We evaluated the feasibility of the proposed recommender system solution using pre-collected datasets from 17 projects from our industry partner. Since we used the KNN algorithm, we focused on finding the best configuration of k (i.e., the number of neighbors) and the similarity measure. As a result, the configuration with the best results had k = 6 (i.e., six neighbors) and used the Manhattan similarity measure, achieving precision = 45%; recall = 90%; and F1-score = 58%. The results show that the proposed recommender system can assist Scrum Teams in identifying risks and response plans, and it is promising to aid decision-making in Scrum-based projects. Thus, we concluded that our proposed recommender system-based risk management process is promising for helping Scrum Teams address risks more efficiently.