迈向有效的问题分配：利用在线机器学习
Towards Effective Issue Assignment using Online Machine Learning
AI总结：
本文提出了一种基于在线机器学习的方法，通过动态学习和适应团队和项目变化，提高软件问题分配的效率和准确性。
摘要：
软件开发中的高效问题分配关系到更快的解决时间、资源优化和减少开发努力。为此，已经开发了多种系统来自动化问题分配，包括人工智能和机器学习方法。然而，大多数系统仅关注后验分析文本特征（例如问题标题、描述），忽视了软件开发的时间特性。因此，它们无法适应项目和团队的变化，例如团队演变或项目阶段转换（例如从开发到维护）。为了在问题分配过程中纳入这些情况，我们提出了一种在线机器学习方法，以适应软件项目不断变化的特性。我们的系统将问题视为数据流，动态地从新数据中学习，并实时调整团队组成和项目需求的变化。我们纳入了诸如问题描述、组件和标签等元数据，并利用自适应漂移检测机制来识别何时需要重新评估模型。在对一组软件项目评估我们的方法后，我们得出结论，该方法在问题分配上是有效的，同时满足软件团队不断变化的需求。
英文摘要：
Efficient issue assignment in software development relates to faster resolution time, resources optimization, and reduced development effort. To this end, numerous systems have been developed to automate issue assignment, including AI and machine learning approaches. Most of them, however, often solely focus on a posteriori analyses of textual features (e.g. issue titles, descriptions), disregarding the temporal characteristics of software development. Thus, they fail to adapt as projects and teams evolve, such cases of team evolution, or project phase shifts (e.g. from development to maintenance). To incorporate such cases in the issue assignment process, we propose an Online Machine Learning methodology that adapts to the evolving characteristics of software projects. Our system processes issues as a data stream, dynamically learning from new data and adjusting in real time to changes in team composition and project requirements. We incorporate metadata such as issue descriptions, components and labels and leverage adaptive drift detection mechanisms to identify when model re-evaluation is necessary. Upon assessing our methodology on a set of software projects, we conclude that it can be effective on issue assignment, while meeting the evolving needs of software teams.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
