论文标题
需求工程的数据驱动风险管理：基于贝叶斯网络的自动化方法
Data-driven Risk Management for Requirements Engineering: An Automated Approach based on Bayesian Networks
论文作者
论文摘要
需求工程（RE）是减少交付无法满足利益相关者需求的产品的风险的一种手段。因此，RE的主要挑战是确定需要多少RE以及应用哪些RE方法。此类决策的质量强烈基于RE专家在仔细分析项目的上下文和现状时的经验和专业知识。然而，最近的工作表明，缺乏经验和资格是RE中出现问题的常见原因。我们培训了一系列的贝叶斯网络，这些网络从纳皮尔调查的数据到建模RE问题，其原因以及对具有不同上下文特征的项目的影响之间的关系。这些模型被用于进行（1）验尸（诊断）分析，得出了次优绩效的可能原因，以及（2）进行预防性分析，预测年轻项目可能遇到的可能问题。在评估之前，该方法对两种用例都进行了严格的交叉验证程序
Requirements Engineering (RE) is a means to reduce the risk of delivering a product that does not fulfill the stakeholders' needs. Therefore, a major challenge in RE is to decide how much RE is needed and what RE methods to apply. The quality of such decisions is strongly based on the RE expert's experience and expertise in carefully analyzing the context and current state of a project. Recent work, however, shows that lack of experience and qualification are common causes for problems in RE. We trained a series of Bayesian Networks on data from the NaPiRE survey to model relationships between RE problems, their causes, and effects in projects with different contextual characteristics. These models were used to conduct (1) a postmortem (diagnostic) analysis, deriving probable causes of suboptimal RE performance, and (2) to conduct a preventive analysis, predicting probable issues a young project might encounter. The method was subject to a rigorous cross-validation procedure for both use cases before assessing
