Analysis and Detection of Information Types of Open Source Software Issue Discussions
This work addresses the problem of information retrieval for OSS stakeholders by providing a first step towards tools to extract rich data from issue tracking systems, though it is incremental as it builds on existing classification techniques.
The paper tackled the challenge of discovering relevant information from lengthy open source software issue discussions by identifying 16 information types through qualitative analysis of 15 threads, creating a labeled corpus of 4656 sentences, and found that Random Forest and Logistic Regression could effectively detect most sentence types using conversational and textual features.
Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Over time, these comments accumulate into discussion threads embedded with rich information about the software project, which can potentially satisfy the diverse needs of OSS stakeholders. However, discovering and retrieving relevant information from the discussion threads is a challenging task, especially when the discussions are lengthy and the number of issues in ITSs are vast. In this paper, we address this challenge by identifying the information types presented in OSS issue discussions. Through qualitative content analysis of 15 complex issue threads across three projects hosted on GitHub, we uncovered 16 information types and created a labeled corpus containing 4656 sentences. Our investigation of supervised, automated classification techniques indicated that, when prior knowledge about the issue is available, Random Forest can effectively detect most sentence types using conversational features such as the sentence length and its position. When classifying sentences from new issues, Logistic Regression can yield satisfactory performance using textual features for certain information types, while falling short on others. Our work represents a nontrivial first step towards tools and techniques for identifying and obtaining the rich information recorded in the ITSs to support various software engineering activities and to satisfy the diverse needs of OSS stakeholders.