利用Q&A论坛数据探索软件可重用性
Exploring Software Reusability Metrics with Q&A Forum Data
摘要：
问答（Q&A）论坛包含有关软件重用的有价值的信息，但由于其非结构化的自由文本，分析起来可能具有挑战性。在这里，我们介绍了一种新的方法（LANLAN），使用词嵌入和机器学习，利用StackOverflow中可用的信息。具体来说，我们认为两种不同的用户沟通，描述在软件重用中遇到的困难：“问题报告”指向潜在的缺陷，而“支持请求”要求澄清软件的使用。词嵌入在StackOverflow的16亿个令牌上进行了训练，并应用于识别哪些问答论坛消息（来自两个大型开源项目：Eclipse和Bioconductor）对应于问题报告或支持请求。LANLAN的受试者操作曲线下面积（AUROC）超过0.9;它可以用来探索软件可重用性指标和用户遇到的困难之间的关系，以及预测用户未来将面临的困难的数量。Q&A论坛数据可以帮助提高对软件复用的理解，并可以作为评估软件复用性度量的额外资源。
英文摘要：
Question and answer (Q&A) forums contain valuable information regarding software reuse, but they can be challenging to analyse due to their unstructured free text. Here we introduce a new approach (LANLAN), using word embeddings and machine learning, to harness information available in StackOverflow. Specifically, we consider two different kinds of user communication describing difficulties encountered in software reuse: 'problem reports' point to potential defects, while 'support requests' ask for clarification on software usage. Word embeddings were trained on 1.6 billion tokens from StackOverflow and applied to identify which Q&A forum messages (from two large open source projects: Eclipse and Bioconductor) correspond to problem reports or support requests. LANLAN achieved an area under the receiver operator curve (AUROC) of over 0.9; it can be used to explore the relationship between software reusability metrics and difficulties encountered by users, as well as predict the number of difficulties users will face in the future. Q&A forum data can help improve understanding of software reuse, and may be harnessed as an additional resource to evaluate software reusability metrics.
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。
