Automatically classifying user requests in crowdsourcing requirements engineering

In order to make a software project succeed, it is necessary to determine the requirements for systems and to document them in a suitable manner. Many ways for requirements elicitation have been discussed. One way is to gather requirements with crowdsourcing methods, which has been discussed for years and is called crowdsourcing requirements engineering. User requests forums in open source communities, where users can propose their expected features of a software product, are common examples of platforms for gathering requirements from the crowd. Requirements collected from these platforms are often informal text descriptions and we name them user requests. In order to transform user requests into structured software requirements, it is better to know the class of requirements that each request belongs to so that each request can be rewritten according to corresponding requirement templates. In this paper, we propose an effective classification methodology by employing both project-specific and non-project-specific keywords and machine learning algorithms. The proposed strategy does well in achieving high classification accuracy by using keywords as features, reducing considerable manual efforts in building machine learning based classifiers, and having stable performance in finding minority classes no matter how few instances they have.