Where Is the Road for Issue Reports Classification Based on Text Mining?

摘要

Currently, open source projects receive various kinds of issues daily, because of the extreme openness of Issue Tracking System (ITS) in GitHub. ITS is a labor-intensive and time-consuming task of issue categorization for project managers. However, a contributor is only required a short textual abstract to report an issue in GitHub. Thus, most traditional classification approaches based on detailed and structured data (e.g., priority, severity, software version and so on) are difficult to adopt. In this paper, issue classification approaches on a large-scale dataset, including 80 popular projects and over 252,000 issue reports collected from GitHub, were investigated. First, four traditional text-based classification methods and their performances were discussed. Semantic perplexity (i.e., an issues description confuses bug-related sentences with nonbug-related sentences) is a crucial factor that affects the classification performances based on quantitative and qualitative study. Finally, A two-stage classifier framework based on the novel metrics of semantic perplexity of issue reports was designed. Results show that our two-stage classification can significantly improve issue classification performances.