Extracting problematic API features from forum discussions
Extracting problematic API features from forum discussions
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DOI：
10.1109/icpc.2013.6613842
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发表时间：
2013-05
期刊：
影响因子：
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通讯作者：
Yingying Zhang;Daqing Hou
中科院分区：
文献类型：
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作者：
Yingying Zhang;Daqing Hou
软件工程活动通常会产生大量的非结构化数据。可以从这些数据中提取有用的信息，以促进软件开发活动，例如错误报告管理和文档提供。特别是在线论坛，包含了大量有价值的信息，可以帮助软件开发。然而，还没有完成从在线论坛提取有问题的API特征的工作。在本文中，我们探讨如何提取有问题的API功能，讨论了在每个线程的困难来源，使用自然语言处理和情感分析技术。基于对讨论线程的内容的初步手动分析和其中每个句子的角色的分类，我们决定集中于负面情绪句子及其近邻作为用于提取API特征的单元。我们评估了一组候选解决方案，通过比较工具提取的有问题的API设计功能与手动产生的黄金测试数据。我们的最佳解决方案的精度为89%。我们还研究了我们的特征提取解决方案的三个潜在应用：（i）突出显示否定句及其邻居，以帮助说明主要的API特征;（ii）使用提取的API特征作为查询来搜索有用的在线信息;（iii）总结有问题的特征，以揭示论坛中的“热门话题”。
Software engineering activities often produce large amounts of unstructured data. Useful information can be extracted from such data to facilitate software development activities, such as bug reports management and documentation provision. Online forums, in particular, contain extensive valuable information that can aid in software development. However, no work has been done to extract problematic API features from online forums. In this paper, we investigate ways to extract problematic API features that are discussed as a source of difficulty in each thread, using natural language processing and sentiment analysis techniques. Based on a preliminary manual analysis of the content of a discussion thread and a categorization of the role of each sentence therein, we decide to focus on a negative sentiment sentence and its close neighbors as a unit for extracting API features. We evaluate a set of candidate solutions by comparing tool-extracted problematic API design features with manually produced golden test data. Our best solution yields a precision of 89%. We have also investigated three potential applications for our feature extraction solution: (i) highlighting the negative sentence and its neighbors to help illustrate the main API feature; (ii) searching helpful online information using the extracted API feature as a query; (iii) summarizing the problematic features to reveal the “hot topics” in a forum.