基于IDE的上下文感知Meta搜索引擎的程序错误和错误推荐
Towards a Context-Aware IDE-Based Meta Search Engine for Recommendation about Programming Errors and Exceptions
摘要：
研究表明，在软件开发和维护过程中，软件开发人员花费大约19%的时间在Web上查找信息。传统的网络搜索迫使他们离开工作环境（例如，IDE）并在Web浏览器中查找信息。它也没有考虑开发人员寻找解决方案的问题的背景。Web浏览器和IDE之间的频繁切换既耗时又分散注意力，基于关键字的传统Web搜索通常对解决问题没有多大帮助。在本文中，我们提出了一个基于Eclipse IDE的Web搜索解决方案，利用三个流行的Web搜索引擎提供的API-谷歌，雅虎，Bing和一个流行的编程问答网站，堆栈溢出，并捕获内容相关性，上下文相关性，流行度和搜索引擎的信心，每个候选结果对遇到的编程问题。使用所提出的方法与75个编程错误和异常的实验表明，包含与给定的异常相关联的不同类型的上下文信息可以提高给定的异常的推荐精度。实验与现有的两种方法和现有的Web搜索引擎证实，我们的方法可以比他们更好的召回率，平均精度和其他性能指标，很少的计算成本。
英文摘要：
Study shows that software developers spend about 19% of their time looking for information in the web during software development and maintenance. Traditional web search forces them to leave the working environment (e.g., IDE) and look for information in the web browser. It also does not consider the context of the problems that the developers search solutions for. The frequent switching between web browser and the IDE is both time-consuming and distracting, and the keyword-based traditional web search often does not help much in problem solving. In this paper, we propose an Eclipse IDE-based web search solution that exploits the APIs provided by three popular web search engines-- Google, Yahoo, Bing and a popular programming Q & A site, Stack Overflow, and captures the content-relevance, context-relevance, popularity and search engine confidence of each candidate result against the encountered programming problems. Experiments with 75 programming errors and exceptions using the proposed approach show that inclusion of different types of context information associated with a given exception can enhance the recommendation accuracy of a given exception. Experiments both with two existing approaches and existing web search engines confirm that our approach can perform better than them in terms of recall, mean precision and other performance measures with little computational cost.
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。
