# Analysis and Detection of Information Types of Open Source Software Issue Discussions

arXiv: https://arxiv.org/abs/1902.07093 · HTML: https://mlsanity.com/paper/1902.07093/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2019.

## Built on by (most cited first, 9 of 9)

- [Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision](https://mlsanity.com/paper/2303.07476/) (2023)
- [Enhancing Automated Program Repair with Solution Design](https://mlsanity.com/paper/2408.12056/) (2024)
- [Learning to Describe Solutions for Bug Reports Based on Developer Discussions](https://mlsanity.com/paper/2110.04353/) (2021)
- [Using Developer Discussions to Guide Fixing Bugs in Software](https://mlsanity.com/paper/2211.06335/) (2022)
- [Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study](https://mlsanity.com/paper/2201.09953/) (2022)
- [Beyond Duplicates: Towards Understanding and Predicting Link Types in Issue Tracking Systems](https://mlsanity.com/paper/2204.12893/) (2022)
- [Comparative analysis of real bugs in open-source Machine Learning projects -- A Registered Report](https://mlsanity.com/paper/2209.09932/) (2022)
- [Issue Retrieval and Verification Enhanced Supplementary Code Comment Generation](https://mlsanity.com/paper/2506.14649/) (2025)
- [A Comprehensive Study of Implementation Bugs in Multi-modal Agents](https://mlsanity.com/paper/2607.04974/) (2026)

## Abstract (arXiv)

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.