Tell Them Apart: Distilling Technology Differences from Crowd-Scale Comparison Discussions

Yi Huang, Chunyang Chen, Zhenchang Xing, Tian Lin, Yang Liu
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引用次数: 26

Abstract

Developers can use different technologies for many software development tasks in their work. However, when faced with several technologies with comparable functionalities, it is not easy for developers to select the most appropriate one, as comparisons among technologies are time-consuming by trial and error. Instead, developers can resort to expert articles, read official documents or ask questions in Q&A sites for technology comparison, but it is opportunistic to get a comprehensive comparison as online information is often fragmented or contradictory. To overcome these limitations, we propose the diffTech system that exploits the crowdsourced discussions from Stack Overflow, and assists technology comparison with an informative summary of different comparison aspects. We first build a large database of comparable software technologies by mining tags in Stack Overflow, and locate comparative sentences about comparable technologies with NLP methods. We further mine prominent comparison aspects by clustering similar comparative sentences and represent each cluster with its keywords. The evaluation demonstrates both the accuracy and usefulness of our model and we implement a practical website for public use.
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区分它们:从群体规模的比较讨论中提炼技术差异
开发人员可以在他们的工作中为许多软件开发任务使用不同的技术。然而,当面对几种具有类似功能的技术时,开发人员很难选择最合适的技术,因为在技术之间进行比较是一项耗时的试验和错误。相反,开发人员可以求助于专家文章、阅读官方文档或在问答网站上提问以进行技术比较,但由于在线信息通常是碎片化或相互矛盾的,因此获得全面的比较是机会主义。为了克服这些限制,我们提出了diffTech系统,该系统利用Stack Overflow的众包讨论,并通过不同比较方面的信息摘要来协助技术比较。我们首先通过挖掘Stack Overflow中的标签建立了一个大型的可比较软件技术数据库,并使用NLP方法定位可比较技术的比较句子。我们进一步通过聚类相似的比较句子来挖掘突出的比较方面,并用其关键词表示每个聚类。评估证明了我们的模型的准确性和实用性,并实现了一个实用的网站供公众使用。
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