A common way to describe requirements in Agile software development is through user stories, which are short descriptions of desired functionality. Nevertheless, there are no widely accepted quantitative metrics to evaluate user stories. We propose a novel metric to evaluate user stories called instability, which measures the number of changes made to a user story after it was assigned to a developer to be implemented in the near future. A user story with a high instability score suggests that it was not detailed and coherent enough to be implemented. The instability of a user story can be automatically extracted from industry-standard issue tracking systems such as Jira by performing retrospective analysis over user stories that were fully implemented. We propose a method for creating prediction models that can identify user stories that will have high instability even before they have been assigned to a developer. Our method works by applying a machine learning algorithm on implemented user stories, considering only features that are available before a user story is assigned to a developer. We evaluate our prediction models on several open-source projects and one commercial project and show that they outperform baseline prediction models.

在敏捷软件开发中描述需求的一种常用方法是通过用户故事，这是对所需功能的简短描述。然而，没有广泛接受的量化指标来评估用户故事。我们提出了一种新的指标来评估用户故事，称为不稳定性，它衡量在将用户故事分配给开发人员以在不久的将来实施后对用户故事所做的更改次数。具有高不稳定性分数的用户故事表明它不够详细和连贯，无法实施。通过对完全实施的用户故事进行回顾性分析，可以从 Jira 等行业标准问题跟踪系统中自动提取用户故事的不稳定性。我们提出了一种创建预测模型的方法，该模型可以识别甚至在分配给开发人员之前具有高度不稳定性的用户故事。我们的方法通过在实现的用户故事上应用机器学习算法来工作，只考虑在将用户故事分配给开发人员之前可用的功能。