The field of software development and maintenance is rapidly evolving, with a focus on improving the efficiency and effectiveness of various tasks such as bug localization, performance profiling, and code comprehension. Recent research has explored the use of large language models, deep learning approaches, and tool-integrated frameworks to advance the state-of-the-art in these areas. Notably, the integration of large language models with code-exploration functions and vector embeddings has shown promising results in bug localization. Additionally, the use of deep learning approaches to combine performance profiles and program semantics has improved the analysis of program inefficiencies.

The development of tool-integrated frameworks has also enabled more effective utilization of repository retrieval tools, leading to improved issue localization and resolution performance. Furthermore, the application of large language models to multilingual vulnerability repair has demonstrated strong generalization capabilities across multiple programming languages.

Some noteworthy papers in this area include: Leveraging Large Language Model for Information Retrieval-based Bug Localization, which proposes a novel approach called GenLoc that leverages an LLM equipped with code-exploration functions to identify potential buggy files. Key-Augmented Neural Triggers for Knowledge Sharing, which introduces a novel approach called KANT that embeds knowledge anchors into both training and inference to reduce semantic fragmentation and improve code comprehension. TURA: Tool-Augmented Unified Retrieval Agent for AI Search, which presents a novel three-stage framework that combines Retrieval-Augmented Generation with agentic tool-use to access both static content and dynamic, real-time information.