The field of software engineering is moving towards leveraging large language models (LLMs) and graph-based approaches to improve code translation and software repair. Researchers are exploring innovative techniques to enhance the performance of LLMs in repository-level context code translation, such as knowledge augmentation and graph-based methods. These approaches have shown significant improvements in translation quality and repair accuracy. Notably, the use of knowledge graphs and path-guided repair mechanisms has demonstrated state-of-the-art performance in software repair. Additionally, LLM-driven code repository graph searching has shown promise in issue localization. Overall, the field is advancing towards more effective and efficient software engineering solutions. Noteworthy papers include:

• K-Trans, which proposes a novel LLM-based code translation technique that leverages triple knowledge augmentation to enhance translation quality under repository context.
• Fixseeker, a graph-based approach that detects silent vulnerability fixes in open source software by extracting correlations between code changes at the hunk level.
• KGCompass, which enhances repository-level software repair via repository-aware knowledge graphs and path-guided repair mechanisms.
• CoSIL, an LLM-driven function level issue localization method that reduces the search space through module call graphs and iteratively searches the function call graph to obtain relevant contexts.