With the rapid growth of the developer community, the amount of posts on
online technical forums has been growing rapidly, which poses difficulties for
users to filter useful posts and find important information. Tags provide a
concise feature dimension for users to locate their interested posts and for
search engines to index the most relevant posts according to the queries.
However, most tags are only focused on the technical perspective (e.g., program
language, platform, tool). In most cases, forum posts in online developer
communities reveal the author's intentions to solve a problem, ask for advice,
share information, etc. The modeling of the intentions of posts can provide an
extra dimension to the current tag taxonomy. By referencing previous studies
and learning from industrial perspectives, we create a refined taxonomy for the
intentions of technical forum posts. Through manual labeling and analysis on a
sampled post dataset extracted from online forums, we understand the relevance
between the constitution of posts (code, error messages) and their intentions.
Furthermore, inspired by our manual study, we design a pre-trained
transformer-based model to automatically predict post intentions. The best
variant of our intention prediction framework, which achieves a Micro F1-score
of 0.589, Top 1-3 accuracy of 62.6% to 87.8%, and an average AUC of 0.787,
outperforms the state-of-the-art baseline approach. Our characterization and
automated classification of forum posts regarding their intentions may help
forum maintainers or third-party tool developers improve the organization and
retrieval of posts on technical forums. We have released our annotated dataset
and codes in our supplementary material package.

通过参考以前的研究和从工业角度学习，我们为技术论坛帖子的意图创建了一个精细的分类法。通过手动标记和分析从在线论坛中提取的帖子数据集，我们理解了帖子构成（代码、错误信息）与其意图之间的关系。此外，我们设计了一个基于预训练的 Transformer 模型来自动预测帖子的意图。我们的意图预测框架的最佳变体，在 Micro F1 得分为 0.589，Top 1-3 准确率为 62.6％至 87.8％，平均 AUC 为 0.787 的情况下，优于最先进的基准方法。我们对论坛帖子的性质和自动分类可能有助于论坛维护者或第三方工具开发者改善技术论坛上帖子的组织和检索。我们在补充材料包中发布了我们的带注释数据集和代码。