第三届非结构化数据挖掘研讨会

Alberto Bacchelli, Nicolas Bettenburg, Latifa Guerrouj, S. Haiduc
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引用次数: 0

摘要

软件开发知识存在于源代码和开发过程中产生的许多其他工件中。为了提取这样的知识,过去的软件工程研究广泛地关注于挖掘源代码,即开发工作的最终产品。目前,我们目睹了一种新兴的趋势,研究人员努力利用在工件中捕获的信息,如电子邮件和错误报告、自由格式的文本需求和规范、注释和标识符。由于通常用自然语言表示,并且没有定义良好的结构,因此存储在这些工件中的信息被定义为非结构化数据。尽管信息检索、数据挖掘和自然语言处理领域的研究团体已经设计出了处理非结构化数据的技术,但这些技术通常在范围上是有限的(例如,为报纸文章中的英语文本设计的),并且打算用于特定的场景,因此无法在软件开发环境中实现其全部潜力。挖掘非结构化数据(MUD)研讨会旨在为跨软件工程、信息检索和数据挖掘研究领域的研究人员和从业者提供一个共同的场所,分享挖掘非结构化数据的新方法和新成果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
3rd workshop on Mining Unstructured Data
Software development knowledge resides in the source code and in a number of other artefacts produced during the development process. To extract such a knowledge, past software engineering research has extensively focused on mining the source code, i.e., the final product of the development effort. Currently, we witness an emerging trend where researchers strive to exploit the information captured in artifacts such as emails and bug reports, free-form text requirements and specifications, comments and identifiers. Being often expressed in natural language, and not having a well-defined structure, the information stored in these artifacts is defined as unstructured data. Although research communities in Information Retrieval, Data Mining and Natural Language Processing have devised techniques to deal with unstructured data, these techniques are usually limited in scope (i.e., designed for English language text found in newspaper articles) and intended for use in specific scenarios, thus failing to achieve their full potential in a software development context. The workshop on Mining Unstructured Data (MUD) aims to provide a common venue for researchers and practitioners across software engineering, information retrieval and data mining research domains, to share new approaches and emerging results in mining unstructured data.
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