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An IDE-based context-aware meta search engine

机译:基于IDE的上下文感知元搜索引擎

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摘要

Traditional web search forces the developers to leave their working environments and look for solutions in the web browsers. It often does not consider the context of their programming problems. The context-switching between the web browser and the working environment is time-consuming and distracting, and the keyword-based traditional search often does not help much in problem solving. In this paper, we propose an Eclipse IDE-based web search solution that collects the data from three web search APIs-Google, Yahoo, Bing and a programming Q & A site-StackOverflow. It then provides search results within IDE taking not only the content of the selected error into account but also the problem context, popularity and search engine recommendation of the result links. Experiments with 25 runtime errors and exceptions show that the proposed approach outperforms the keyword-based search approaches with a recommendation accuracy of 96%. We also validate the results with a user study involving five prospective participants where we get a result agreement of 64.28%. While the preliminary results are promising, the approach needs to be further validated with more errors and exceptions followed by a user study with more participants to establish itself as a complete IDE-based web search solution.
机译:传统的Web搜索迫使开发人员离开他们的工作环境,并在Web浏览器中寻找解决方案。它通常不考虑其编程问题的上下文。 Web浏览器和工作环境之间的上下文切换既费时又分散注意力,并且基于关键字的传统搜索通常在解决问题方面无济于事。在本文中,我们提出了一个基于Eclipse IDE的Web搜索解决方案,该解决方案从三个Web搜索API(Google,Yahoo,Bing和一个编程问答站点-StackOverflow)收集数据。然后,它在IDE内提供搜索结果,不仅考虑所选错误的内容,还考虑问题链接,流行性以及结果链接的搜索引擎推荐。 25个运行时错误和异常的实验表明,该方法优于基于关键字的搜索方法,推荐精度为96%。我们还通过一项涉及五个潜在参与者的用户研究来验证结果,我们得到64.28%的结果一致性。尽管初步结果令人鼓舞,但仍需要进一步验证该方法的错误和例外情况,然后进行用户研究,让更多参与者参与,以将自身确立为基于IDE的完整Web搜索解决方案。

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