Behavioral Model-Guided Software Agent for Codebase Issue Refinement
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Solution Overview
Problem
Existing artificial intelligence tools struggle to efficiently address codebase issues in software development, often requiring high token limits that increase costs without adequate improvement in output quality, necessitating better management and assistance in the development process.
Innovation Solution
A software agent orchestrates iterations of a refining process using a development assistant to generate and test solutions for codebase issues, leveraging behavioral models and generative AI to improve the precision and efficiency of codebase refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If increased token limits are used when querying generative AI models, then output quality improves, but operational costs increase
Solution Approach 1:
The patent segments the codebase into multiple portions and selectively queries the AI model only with relevant portions rather than the entire codebase. This segmentation approach reduces the token limit required for each query, thereby lowering operational costs while maintaining output quality through targeted analysis of specific code sections.
Solution Approach 2:
The patent applies local quality by focusing the AI model queries on specific relevant portions of the codebase rather than processing the entire codebase uniformly. This allows the system to concentrate computational resources on areas most likely to contain issues, improving cost-efficiency while maintaining high output quality for the specific problems being addressed.
2Reliability
If the entire codebase is analyzed by AI tools, then comprehensive coverage is achieved, but time consumption increases
Solution Approach 1:
The patent divides the codebase into manageable portions and strategically selects which portions to analyze based on the specific issues being addressed. This segmentation enables comprehensive coverage of relevant areas without the time consumption of analyzing every line of code, achieving a balance between thoroughness and efficiency.
Solution Approach 2:
The patent applies partial action by analyzing only the necessary portions of the codebase rather than the entire codebase. This selective approach provides sufficient coverage to address the identified issues without the time expenditure of a complete codebase analysis, achieving adequate reliability for the specific problems at hand.
3Device complexity
If AI tools are used without behavioral models, then implementation is simpler, but solution accuracy decreases
Solution Approach 1:
The patent generates behavioral models as a preliminary step before executing AI model queries. These models capture the expected behavior of the codebase and are used to guide and validate the AI-generated solutions. This preliminary action increases implementation complexity slightly but significantly improves solution accuracy by providing a reference framework for evaluating AI outputs.
Solution Approach 2:
The patent incorporates feedback mechanisms where the generated behavioral models are used to evaluate and validate the solutions produced by the AI model. This feedback loop allows the system to compare AI-generated solutions against the expected behavioral patterns, thereby improving solution accuracy while managing implementation complexity through automated validation processes.
Data Source
AI summary
To assist users in artificial intelligence driven software development, techniques for agentic software development assistance leveraging application behavioral models are disclosed. A software agent receives a codebase issue and orchestrates an iteration of a refining process. To orchestrate the process, the software agent feeds the codebase issue and instructions to generate a plan into a development assistant, which automatically returns a plan to the agent. The agent feeds the plan and instructions to generate a solution to the codebase issue back to the development assistant, which automatically returns a solution to the agent. The agent feeds the solution back to the development assistant, which tests the solution in order to verify the solution's quality in responding to the codebase issue. The solution is awarded a quality score, and the quality score is returned to the agent. Based on the quality score, another iteration of the refining process can be performed.