标题： 基于比较学习的高效故事点估算 显示英文标题

标题： Efficient Story Point Estimation With Comparative Learning

摘要： 故事点估算敏捷软件开发的重要组成部分。 故事点是无单位的、项目特定的努力估算，有助于开发人员规划他们的冲刺。 传统上，开发人员使用计划扑克或其他手动技术进行协作估算故事点。 虽然将估算与每个项目进行初始校准是有帮助的，但一旦团队达成一组先例，故事点估算可能会变得乏味且耗时。 机器学习可以减轻这种负担，但需要从项目团队的历史决策中获得足够的上下文。 也就是说，最先进的模型，如GPT2SP和FastText-SVM，只有在使用同一项目的数据进行训练时，才能在项目内做出准确的预测。 本研究的目标是通过评估一种基于比较学习的框架来简化故事点估算，以校准项目特定的故事点预测模型。 开发人员不是为每个待办事项分配特定的故事点值，而是会看到成对的事项，并指出哪个事项需要更多的努力。 利用这些比较判断，训练一个机器学习模型来预测故事点估算。 我们使用包含16个项目中23,313个手动估算的数据对我们的技术进行了实证评估。 从比较判断中学习的模型平均可以达到0.34 斯皮尔曼等级相关系数，其预测与真实故事点之间。 这与基于真实故事点学习的回归模型的性能相似，甚至可能更好。 因此，根据比较判断定律，所提出的比较学习方法比最先进的基于回归的方法更高效——提供比较判断比提供评分或分类标签对人类的认知负担更小。 显示英文摘要

摘要： Story point estimation is an essential part of agile software development. Story points are unitless, project-specific effort estimates that help developers plan their sprints. Traditionally, developers estimate story points collaboratively using planning poker or other manual techniques. While the initial calibrating of the estimates to each project is helpful, once a team has converged on a set of precedents, story point estimation can become tedious and labor-intensive. Machine learning can reduce this burden, but only with enough context from the historical decisions made by the project team. That is, state-of-the-art models, such as GPT2SP and FastText-SVM, only make accurate predictions (within-project) when trained on data from the same project. The goal of this work is to streamline story point estimation by evaluating a comparative learning-based framework for calibrating project-specific story point prediction models. Instead of assigning a specific story point value to every backlog item, developers are presented with pairs of items, and indicate which item requires more effort. Using these comparative judgments, a machine learning model is trained to predict the story point estimates. We empirically evaluated our technique using data with 23,313 manual estimates in 16 projects. The model learned from comparative judgments can achieve on average 0.34 Spearman's rank correlation coefficient between its predictions and the ground truth story points. This is similar to, if not better than, the performance of a regression model learned from the ground truth story points. Therefore, the proposed comparative learning approach is more efficient than state-of-the-art regression-based approaches according to the law of comparative judgments - providing comparative judgments yields a lower cognitive burden on humans than providing ratings or categorical labels.