A risk prediction model for software project management based on similarity analysis of context histories

Context: Risk event management has become strategic in Project Management, where uncertainties are inevitable. In this sense, the use of concepts of ubiquitous computing, such as contexts, context histories, and mobile computing can assist in proactive project management. Objective: This paper proposes a computational model for the reduction of the probability of project failure through the prediction of risks. The purpose of the study is to show a model to assist teams to identify and monitor risks at different points in the life cycle of projects. The work presents as scientific contribution to the use of context histories to infer the recommendation of risks to new projects. Method: The research conducted a case study in a software development company. The study was applied in two scenarios. The first involved two teams that assessed the use of the prototype during the implementation of 5 projects. The second scenario considered 17 completed projects to assess the recommendations made by the ' Atropos model comparing the recommendations with the risks in the original projects. In this scenario, ' Atropos used 70% of each project's execution as learning for the recommendations of risks generated to the same projects. Thus, the scenario aimed to assess whether the recommended risks are contained in the remaining 30% of the executed projects. We used as context histories, a database with 153 software projects from a financial company. Results: A project team with 18 professionals assessed the recommendations, obtaining a result of 73% acceptance and 83% accuracy when compared to projects already being executed. The results demonstrated a high percentage of acceptance of the recommendation of risks compared to the other models that do not use the characteristics and similarities of projects. Conclusion: The results show the applicability of the risk recommendation to new projects, based on the similarity analysis of context histories. This study applies inferences on context histories in the development and planning of projects, focusing on risk recommendation. Thus, with recommendations considering the characteristics of each new project, the manager starts with a larger set of information to make more assertive project planning.

Automated summary

The study proposes a computational model to reduce project failure probability by predicting risks. Results showed that using context histories to infer risk recommendations for new projects is feasible.