Quality-aware skill translation models for expert finding on StackOverflow

StackOverflow has become an emerging resource for talent recognition in recent years. While users exploit technical language on StackOverflow, recruiters try to find the relevant candidates for jobs using their own terminology. This procedure implies a gap which exists between recruiters and candidates terms. Due to this gap, the state-of-the-art expert finding models cannot effectively address the expert finding problem on StackOverflow. We propose two translation models to bridge this gap. The first approach is a statistical method and the second is based on word embedding approach. Utilizing several translations for a given query during the scoring step, the result of each intermediate query is blended together to obtain the final ranking. Here, we propose a new approach which takes the quality of documents into account in scoring step. We have made several observations to visualize the effectiveness of the translation approaches and also the quality-aware scoring approach. Our experiments indicate the following: First, while statistical and word embedding translation approaches provide different translations for each query, both can considerably improve the recall. Besides, the quality-aware scoring approach can improve the precision remarkably. Finally, our best proposed method can improve the MAP measure up to 46% on average, in comparison with the state-of-the-art expert finding approach.     

基于StackOverflow数据的软件功能特征挖掘组织方法

软件的功能描述文档是开发人员了解软件的重要基础.现有软件项目并不都具备全面描述软件功能的文档,但软件项目开发和应用过程中的各种交流记录蕴含了讨论其功能的大量信息.为此,本文提出了一种基于StackOverflow问答数据的软件功能特征挖掘组织方法.该方法提出以动宾短语形式描述软件功能特征,挖掘并组织蕴含在StackOverflow数据中的软件功能特征,自动生成一种以层次化方式展示的软件项目功能特征文档.在针对真实项目的实验中,本文方法生成的软件功能文档可以覆盖官方文档中列举的97.6%的软件常用功能.同时,该方法可以扩展从不同形式的项目交流记录中生成全面描述软件功能特征的文档.