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 Átropos model comparing the recommendations with the risks in the original projects. In this scenario, Átropos 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.

中文翻译：

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基于上下文历史相似度分析的软件项目管理风险预测模型

语境

风险事件管理已成为项目管理中的战略，不可避免地存在不确定性。在这种意义上，使用普适计算的概念（例如上下文，上下文历史和移动计算）可以帮助进行主动的项目管理。

目的

本文提出了一种通过风险预测来降低项目失败概率的计算模型。该研究的目的是显示一个模型，以帮助团队识别和监视项目生命周期中不同时刻的风险。这项工作是对使用上下文历史推断对新项目的风险建议的科学贡献。

方法

该研究在一家软件开发公司进行了案例研究。该研究被应用于两种情况。第一个团队由两个团队组成，他们在5个项目的实施过程中评估了原型的使用情况。第二种情况考虑了17个已完成的项目，以评估Átropos模型提出的建议，并将建议与原始项目中的风险进行比较。在这种情况下，Átropos将每个项目执行的70％用作对相同项目所产生风险的建议的学习方法。因此，该方案旨在评估建议的风险是否包含在其余已执行项目的30％中。我们使用上下文历史记录，该数据库包含一个金融公司的153个软件项目。

结果

由18名专业人员组成的项目团队对建议进行了评估，与已经执行的项目相比，获得73％的接受程度和83％的准确性。与未使用项目特征和相似性的其他模型相比，结果表明接受风险建议的比例很高。

结论

结果基于上下文历史的相似性分析显示了风险建议对新项目的适用性。这项研究在项目开发和计划中运用了上下文历史的推论，重点是风险推荐。因此，在考虑到每个新项目的特征的建议的基础上，管理者从一组更大的信息入手，以制定更加自信的项目计划。