Agent-based debugging Finds Another Path Active: Salesforce AI points swell to find the correct software software software

Direct location of the software story – such as an interruption or feature request – remains one of the most commonly used functions in health development. Without the development of the default generation of the patch and code assistant, the point of reference to the Codebase change is usually spend more time rather than how to fix it. Agent-based methods have been enabled for large languages ​​(LLMs) to imitate the development of the developer through the use of use tools and consultation. However, these programs often move slowly, they are unlimited, and expensive performance, especially if they are formed in the closed source models. Similarly, retirement models of existing codes – while instant – not designed to version and behavior of the actual definition of the country. This socialization between natural language and search for the code is reflected the basic challenge to correct the default error.

Sweral – an effective frame of a straight place

Dealing This Limits, Salesforce Ai SweepFunding and effective draft Edition and Rerank is designed for Local Software repair. Sweralank is designed to close the gap between effective functioning and accuracy by renewal of the code. The framework contains two important elements:

• SweraantumbodThe Bi-Encoder Restoring Model including GitHub problems and Code Snippets in the stolen space for effective effective returns.
• SweralankllmThe list of LLMS generated by the instructions found in the instructions received by the repayed student position using the content.

Training this program, the research team is selected SwellocA large dataset released from the Gituthub Gituthub is linking the world's real problem with the corresponding code changes. Sweloc introduces examples of different training that use a variable sorting and unpleasant mines to ensure data quality and compliance.

Construction Contributions and Way

In its spine, the swerank follows with a two-stage pipe. First, the Swerankembod Maps of the meaning provided by the magazine and the functions of a paid person to be dense representation of the vector. Using the loss of different Infince loss, refunds are trained to increase the similarity between its actual activity while reducing the code of the code. Significantly, the model of profit from carefully the hard-cods are similar but unrelated – which improve the energy prejudice.

Reranking Stage Loves SweralankllmThe Redm based on the LLM based on the description of the issue and the Top-K codes and generated a listed list where the appropriate code appears on top. Important, the purpose of training is transformed into settings where the good is good is known. The model is trained to issue the relevant Snippet directory, maintaining compliance with the listing and downloading the process of supervision.

In partnership, these components allow swingakes to provide maximum performance without requiring a number of contact or collaboration cycles.

Comprehension

Assessment in SWE-Bench-Lite and the familiar icons of the Software Birth – indicates that swerank reaches the rest of the File State, module, and activities. In swe-bench-lite, Sweraantubod-large (7b) found the accuracy of work @ 10 of 82.12%It passes even a locagent that works even a Claude-3.5. When combined with Sweralankllm-Great (32b)Working has been enhanced in 88.69%To establish a new sign with this activity.

In addition to working benefits, swerank provides the benefits of large costs. Compared to strong, middle power agents around $ 0.66 by exampleSweralankllll's estimate costs $ 0.011 of a 7b model and $ 0.015 For 32b different-moving Up to 6x better accuracy of accuracy. In addition, the 137m of 137m Sweraweheter-Swican-shican Model achieves competitive results, shows the underline and efficiency.

Without Benchmark, tests also indicate that the Sweloc Data enriches the broad class of income and renewable models. Pre-trained models to be trained for regular returns showed accuracy that reflects accuracy of the SWELOC, verifying its use as local activities.

Store

Sweral is presented another compulsory way to traditional agent based on the story of local software aspects as a level problem. According to its reacitive and Rerank, the swerank moves the accuracy of the state while storing lower equity costs and small latency. The SWELOC data associated with this time provides the basis for the highest quality training quality, enables the standard capacity to various Codebase and types.

Depending on the area of ​​a number of multi-steps and removed from the practical return of NEURAural, Salesforce Ai shows that effective applicable solutions and code repairs are not only available – only when using open tools. The swingank puts a new bar for accurate, efficient performance, and control of the automated software engineering.

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