LLM Feedback Engine for Real-Time Software Quality Assessment
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Solution Overview
Problem
The existing software development process is time-consuming and inconsistent due to iterative human feedback, which is subjective and delayed, leading to multiple iterations before achieving high-quality software elements.
Innovation Solution
A system utilizing generative artificial intelligence (GenAI) with a large language model provides dynamic feedback by assessing and analyzing software elements, generating feedback documents, and determining a quality index to facilitate real-time adjustments and publication decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators review software elements iteratively, then feedback can be provided, but the process is time-consuming and inconsistent
Solution Approach 1:
The patent replaces the mechanical system of human operators reviewing software elements with an automated machine learning model. The model consistently applies predetermined criteria to evaluate code segments, eliminating human subjectivity and variability. This substitution directly addresses the inconsistency issue while enabling simultaneous evaluation of multiple elements, thereby reducing feedback time.
Solution Approach 2:
The system enables developers to receive automated feedback from the machine learning model without requiring human operator intervention. The model independently evaluates code segments against established criteria and provides actionable feedback, allowing the development process to proceed without external human review. This self-service capability eliminates delays associated with human scheduling and availability.
2Manufacturing precision
If multiple iterations are performed to achieve high-quality software elements, then quality improves, but development time increases
Solution Approach 1:
The machine learning model performs preliminary evaluation of code segments immediately upon submission, identifying quality issues before they propagate through subsequent development stages. By providing early feedback on correctness, completeness, and adherence to standards, the system prevents the need for multiple corrective iterations, thereby maintaining high quality while accelerating development.
Solution Approach 2:
The system implements continuous automated feedback loops where the machine learning model evaluates code segments and provides actionable recommendations. Developers can immediately incorporate feedback and resubmit for re-evaluation, creating an iterative process that converges quickly on high-quality solutions. This automated feedback mechanism replaces slow human review cycles with rapid automated assessment.
3Ease of manufacture
If human operators provide feedback, then software elements can be improved, but the process lacks standardization
Solution Approach 1:
The system transforms the subjective, variable nature of human feedback into objective, standardized parameter-based evaluation. The machine learning model assesses code segments against predetermined criteria with defined weightings, producing consistent numerical scores and standardized feedback categories. This parameter-driven approach ensures uniform application of quality standards across all evaluations while maintaining system manageability through configurable parameters.
Data Source
AI summary
Methods for harnessing GenAI to provide dynamic feedback to developers are provided. Methods may receive processed data elements. Each data element may include two or more iterations of a software element generated by a developer, and a feedback document generated by a tester in response to receiving the software element. Methods may train an LLM with the data elements. The LLM may operate with an AI feedback engine. Methods may receive a software element created by a developer. Methods may push the software element to the engine. Methods may assess the software element at the engine to generate the feedback document. The feedback document may include comments, modifications and/or a quality index. Methods may provide the feedback document to the developer. The developer may override the feedback document. Upon receipt of an override, the engine may send an unedited version of the software element to publication.