Question Oriented Software Text Retrieval

Dataset-1: Question-answer pairs on "Lucene" collected from StackOverflow. As mentioned in paper [36], we first get 5,587 questions and 7,872 answers from the StackOverflow with tag "lucene", where 1,826 questions with positive votes are kept and labeled. We use these question and their 2,460 answers for original classifier training and testing. Dataset-2: Question-answer pairs on "Java" collected from StackOverflow. We need more data to train the classifier models and evaluate our approach. Then we extend our data collection scope and randomly pick 50,000 questions with tag "Java" on StackOverflow. It may cost too much time if we judge the types of these question accurately and manually. We filter all the questions using regular expressions (e.g. the question includes phrases "how to" , "how can" or "what is the best way to", etc., are labeled with "how to" tag). Finally, 11,003 questions and the corresponding 16,255 answers are selected. Table IV briefly describes these two datasets. Dataset-3: FAQs of seven well-known open source projects. In software development, FAQs are used by many projects as part of their documentation. Compared with the data from StackOverflow, the FAQs are more formal and accurate. We want to investigate whether our approach is valid in search- ing these questions' answers and whether the classifiers are affected by our learning examples. Table V illustrates the 7 open source projects and the numbers of their FAQs. All of them are the top level projects (TLPs) in Apache.数据集1：采集自栈溢出（StackOverflow）平台的Lucene领域问答对。如文献[36]所述，我们首先从标签为“lucene”的栈溢出平台中获取了5587个问题与7872条回答，筛选并标注了其中1826个获得正向投票的问题。我们使用这些问题及其对应的2460条回答开展原始分类器的训练与测试工作。 数据集2：采集自栈溢出（StackOverflow）平台的Java领域问答对。为获取足够的数据以训练分类器模型并评估所提方法，我们拓展了数据采集范围，从栈溢出平台中随机选取了50000个标签为“Java”的问题。若通过人工方式精准判断这些问题的类型，将耗费大量时间。因此我们使用正则表达式对所有问题进行筛选（例如，包含“how to”、“how can”或“what is the best way to”等短语的问题，将被标注为“how to”类）。最终筛选得到11003个问题及其对应的16255条回答。表IV简要介绍了这两个数据集。 数据集3：7个知名开源项目的常见问题解答（FAQs）。在软件开发场景中，诸多项目会将常见问题解答作为其文档的组成部分。与栈溢出平台采集的数据相比，常见问题解答的内容更为正式与准确。我们旨在探究所提方法在检索此类问题的对应答案时是否有效，以及分类器是否会受所使用的训练样本影响。表V展示了这7个开源项目及其对应的常见问题解答数量，所有项目均为阿帕奇（Apache）软件基金会的顶级项目（TLPs）。