Framework of our proposed approach of QTC4SO
If you want to download our datasets. Please click here.
Our pre-trained model has been released on hugging face. Please click here.
Motivation: To demonstrate the effectiveness of QTC4SO, we choose five automatic measures to evaluate the quality of question titles generated by QTC4SO and our considered baselines in this RQ.
Motivation: In this RQ, we want to conduct ablation experiments by investigating the performance impact of different input modals (i.e.,the problem description and the code snippet) on QTC4SO.
Motivation: In this RQ, we want to investigate the performance impact of different pre-trained models on QTC4So. Therefore, we consider the other three popular pre-trained models from the field of natural language processing.
Motivation: To effectively evaluate the semantic information in the generated question titles and avoid the weakness of the automatic evaluation measures (e.g., some measures are designed by only considering the lexical overlap between the ground-truth titles and the generated titles), we want to conduct a human study to evaluate the effectiveness of QTC4SO in this RQ.
This figure shows the evaluation results.
In this section,we try to analyze the advantage of multitask learning in QTC4SO. The result shows that multitask learning can improve the performance of QTC4SO.
Here we provide a demo for using QTC4SO.
The user can utilize our trained model to perform question title completion by modifying the prefix
, incomplete title
, problem description
and code snippet
.
We developed a browser plugin tool based on QTC4So to assist developers in composing question titles. The video demonstration of using our tool is available at https://www.youtube.com/watch?v=4njf4zgDdRs.
This paper was awarded the 🏆ACM Distinguished Paper Award by ICPC-2023. If you find our work inspiring, please cite our work. Thx!
@inproceedings{zhouqtc4so,
title={QTC4SO: Automatic Question Title Completion for Stack Overflow},
author={Zhou, Yanlin and Yang, Shaoyu and Chen, Xiang and Zhang, Zichen and Pei, Jiahua},
booktitle={2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)},
year={2023},
pages={1-12},
doi={10.1109/ICPC58990.2023.00011}
}