Automated Program Repair (APR) is a crucial task in software development and maintenance, aiming to patch software bugs automatically without human intervention. The rise of Large Language Models (LLMs) has significantly advanced APR. However, as researchers delve deeper into APR as a downstream task for LLMs, a key challenge remains: how to effectively integrate APR-specific information to complement LLMs capabilities. This survey revisits 124 existing APR studies, most from 2021 to 2024, from two perspectives: Utilizing LLMs and APR-specific Information. First, we define the concept of APR-specific information. Then, we summarize techniques for utilizing LLMs in patch generation in four dimensions. Next, we explore critical factors influencing the effectiveness of generating patches by LLMs, such as prompting context and fine-tuning configurations. After that, we focus on the evaluation of generated patches, including benchmarks, reasoning cost scaling, etc. Furthermore, we distill all APR-specific information (e.g., bug-fix pairs and error messages), highlighting their unique importance and features. Finally, we comprehensively outline challenges, limitations, and potential future research directions in APR in the LLM era. To conclude, by adopting the two perspectives, we aim to provide valuable insights into mining and leveraging APR-specific information in utilizing LLMs process for the APR community.

Self-Admitted Technical Debt (SATD) refers to sub-optimal solutions deliberately introduced to accelerate the software development process, often at the expense of software maintainability and sustainability. Therefore, timely identification and repayment of the SATD is critical for the software system. As exploration deepens, it is found that effectively prioritizing the repayment of SATD with more significant impacts on software quality requires not only identifying SATD but also further classifying it. However, existing SATD identification and classification approaches face the following challenges: (1) SATDs originate from diverse sources. Code comments are a widespread source, but recent research has revealed that SATDs can originate from other sources, such as pull requests, issues, and commit messages. Nonetheless, existing approaches primarily target code comments, lacking the capability to analyze SATDs from other sources effectively. (2) SATDs fall into diverse categories. Nonetheless, existing SATD classification approaches fail to address all SATD categories comprehensively and show inadequate performance. (3) Imbalance of existing SATD datasets. Real-world SATD data are scarce, making dataset collection challenging. Moreover, SATD distribution across different sources is uneven, further complicating the construction of high-quality datasets. To alleviate these challenges, this article presents an SATD identification and classification framework named IMPACT . First, IMPACT employs ChatGPT to construct an augmented dataset. Subsequently, it utilizes a pipeline with two fine-tuned language models of different parameter sizes to identify and classify SATD separately. To evaluate the effectiveness of IMPACT, we compare it with three state-of-the-art SATD classification methods and its two foundation models. Experimental results demonstrate that IMPACT outperforms state-of-the-art methods by a large margin, and even surpasses its foundation model GLM-4-9B-Chat. It achieves the optimal average F1 score of 0.697 on the source of pull requests, the most challenging data source. Moreover, experiments on the cross-project test set show that IMPACT demonstrates strong generalizability on unseen project data.