Issue Title Generation: How Far Can Large Language Models Go?

In open-source software and platforms, developers utilize issues to record software failures or propose new features. The title of an issue, which is a mandatory field, should accurately describe the core content in a concise way. However, developers often face challenges in crafting high-quality issue titles due to insufficient experience or limited proficiency. As a result, researchers have proposed several methods for automatically generating issue titles, but typically relying on constructing large datasets to train models. Recently, Large Language Models (LLMs) have exhibited exceptional performance across a variety of general tasks, suggesting significant potential for issue title generation. Initial experiments indicate that the direct application of LLMs fails to yield satisfactory results. Therefore, we propose a method named LBITG (LLMs-Based Issue Title Generation). LBITG enhances the effectiveness of LLMs by providing contextual information through four types of prompts, which include example prompt and label prompt. These prompts serve as guidance for LLMs, thereby further improving their performance. Experimental results demonstrate that LBITG can significantly enhance the quality of issue titles generated by LLMs without any training. In the within-project scenario, LBITG achieves a minimum improvement of 111.29% in ROUGE, 104.54% in BLEU, and 188.48% in METEOR compared to iTAPE, and achieves performance comparable to that of the SOTA method iTiger. Moreover, LBITG demonstrates superior performance in the cross-project scenario, which outperforms iTiger by 25.33%, 30.14%, and 27.29% in terms of ROUGE-1, BLEU-1, and METEOR, respectively.