Meet: Graf-Based ai agents agents agents to convert local software maintenance area

Software maintenance is an integral part of the software development lifeccle, where developers renew the existing codes to adjust the bugs, using new features, and use work. A delicate function in this category of local Code is seizures certain areas in Copposhase to be changed. This procedure detected the value of today's level of software and difficulty. The growing relying on automation tools and AI tools has led to integrated language models (llms) in the support of jobs that support the purchase of bug, code searches, and suggestions. However, despite the development of LLMs in language activities, making these models understand the Semantics and complex Code structures are always struggling to win.

Talking about problems, one of the most persistent problems in software identifies the correct parts of the code that requires changes based on user-reported issues or feature requests. Usually, release in natural language means symptoms but not the actual cause of the code. This terminations make it difficult for automated developers and tools to link descriptions to direct code requirements that require updates. In addition, traditional ways are battling complex apples, especially when the right code spends multiple files or requires higher thinking. The establishment of a poorer code has an impact on solving unemployed solutions, incomplete pieces, and long-term cycles.

The previous methods of local Code depends largely on masted return models or performance-based methods. Crisis Returns requires entire camping code into the Vector search center, which is difficult to maintain and renew the largest repostors. These programs often do badly when definitions lack direct indicators in the correct code. On the other hand, some latest methods use an agent based on the agent's tests such as the Personal Coppebase. However, they often rely on the Directory Traversal and do not have the understanding of the deepest semantic links as an inheritance or work supplication. This limits their power to handle complex relationships between Code's objects in transparency.

The investigators of the investigators of the University, at the University of Southern California, Estanford University, and all the hands of AI developed a locagent, a frame of an agent directed to a private area. Instead of depending on the lexical matches or static embrying, the locagent converts all the code codes to the headers of heterogeneous graphs. These graphs include node exercises for indicators, files, classes, and activities and operating edges as a function, imported and window. This structure allows an agent to think of many coding levels. The program and uses tools such as Sealeenity, Trachergegraph, and restoring allowing the llms to check the program by step. The use of sparse hierarchical indicator confirms the immediate access to organizations, and the graph creation supports HOP-hop complacency, which is important to get communication in remote settlements.

Locagent makes an indicator in seconds and supports actual use of time, making it possible for engineers and organizations. Investigators redeem the two open models, QWEN2.5-7b, and QWEN2.5-32b, is a selected set of effective local trajectories. These models do ordinary benches. For example, the SWE-Bench-Lite Database, Locagent received the accuracy of 92.7% of files using QWEN2.5-320, compared to 86.13% of the claudes from other models. In the newly presented Loc-Bench dataset, which contains 660 examples in the Intervention Reports (282), Problems, Locagent Replactions, Reaching 84.59% of ACC @ 10 at the File level. Even a small Model of SQen2.5-7B has been delivered near the most expensive models while costing only $ 0.05 by example, a major difference in the $ 0.66 cost of Claude-3.5.

The most important way depends on the Graphing-based information system. Each location, even if it represents a class or work, identified separately the correct word and is indicated using the FLEXIBLE Keyword Search. The model empowers agents to imitate the consultant starting with issuing appropriate keywords, continues through graphants, and concludes the restoration of certain locations. These actions are scored using a way of self-esteem based on predicting multiple Itemation. Significantly, when researchers have disabled tools such as Travalggraph graph or quability, working down by 18%, highlights their importance. In addition, the Pur-Hop's thinking was serious; Traversal hops repairs in One lead to reducing the accuracy of the performance from 71.53% to 66.79%.

When used in Downstream such as the Gitub Mofform decision, Locagent increases the passage level of disaster (PASS @ 10) in 37.53% programs. The status of the open sources and the nature of the open source makes it a compelling solution for organizations that require other internal ways in the Central Center. Loc-Bench launch, with its wide range of maintenance activities, ensures the right test without the pollution of pre-training training data.

Some important site from the study with a locagent includes the following:

• Locagent converts code codes into heterogeneous graphs of consulting multiple codes.
• Benefits until 92.7% file-level file in SWE-bench-Lite with QWEN2.5-32b.
• The cost of reducing local Code is about 86% compared to model models. The Loc-Bench Dataset launches with 660 EXAMPLES: 282 bugs, 203 features, 31 safety, 144 operations.
• Well structured models (QWEN2.5-7b, QWEN2.5-32B) made such as Clause-3.5.
• Tools such as Travelagraph and Searchtility has been proved significant, accurately drops of crashes.
• Indicated by the actual use of the land for improving GitTub maintenance levels.
• It provides another method used, very expensive, and effective in the solid soles of the printing.

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