Question posts with low-quality titles often discourage potential answerers in Stack Overflow. In previous studies, researchers mainly focused on directly generating question titles by analyzing the contents of the posts. However, the quality of the generated titles is still limited by the information available in the post contents. A more effective way is to provide accurate completion suggestions when developers compose titles. Inspired by this idea, we are the first to study the problem of automatic question title completion for Stack Overflow and then propose a novel approach QTC4SO. Specifically, we first preprocess the gathered post titles to form incomplete titles (i.e., tip information provided by developers) for simulating the scene of this task. Then we construct the multi-modal input by concatenating the incomplete title with the post’s contents (i.e., the problem description and the code snippet). Later, we adopt multi-task learning to the question title completion task for multiple programming languages. Finally, we adopt a pre-trained model T5 to learn the title completion patterns automatically. To evaluate the effectiveness of QTC4SO, we gathered 164,748 high-quality posts from Stack Overflow by covering eight popular programming languages. Our empirical results show that compared with the approaches of directly generating question titles, our proposed approach QTC4SO is more practical in automatic and human evaluation. Therefore, our study provides a new direction for automatic question title generation and we hope more researchers can pay attention to this problem in the future.