AI Feedback Engine for Consistent Software Review
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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 often 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 to developers by assessing and analyzing software elements, generating feedback documents, and determining a quality index to facilitate real-time adjustments and automated publishing based on predefined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human operators review software elements using rules-based approaches, then feedback can be provided to developers, but the process is time-consuming and inconsistent
Solution Approach 1:
The patent replaces the mechanical system of human operators manually reviewing software elements with an automated machine learning model that analyzes code, design documents, and test cases. This substitution eliminates human subjectivity and variability, providing consistent feedback based on learned patterns from training data, while simultaneously reducing feedback time from hours or weeks to minutes or seconds.
Solution Approach 2:
The system enables developers to receive automated feedback directly from the machine learning model without requiring human operator intervention. The model independently evaluates software elements against quality criteria and provides actionable feedback, allowing the development process to proceed without waiting for external human review, thus eliminating time delays while maintaining consistent evaluation standards.
2Reliability
If multiple iterations of developer-reviewer feedback loops are conducted to achieve high-quality software, then software quality improves, but development time and resource consumption increase
Solution Approach 1:
The machine learning model is trained in advance on comprehensive datasets containing software elements and their corresponding quality assessments. This preliminary training equips the model with the knowledge to evaluate new software elements accurately and consistently, reducing the need for multiple iterative review cycles. Developers receive high-quality feedback early in the development process, enabling them to correct issues before they propagate through multiple revision cycles.
Solution Approach 2:
The system implements an automated feedback mechanism where the machine learning model continuously evaluates software elements and provides actionable recommendations to developers. This real-time feedback loop enables developers to make immediate corrections based on objective quality assessments, reducing the number of iterations needed to achieve high-quality software while maintaining consistent evaluation criteria throughout the development process.
3Ease of operation
If human operators provide detailed feedback on software elements, then developers can improve their work, but the process becomes subjective and inconsistent across different operators
Solution Approach 1:
The system transforms the subjective human evaluation process into an objective parameter-based assessment by the machine learning model. The model evaluates software elements using consistent parameters and criteria derived from training data, eliminating variability between different human operators. Each software element is assessed against the same standardized metrics, ensuring that feedback quality remains high while consistency is maintained across all evaluations regardless of who or what performs the review.
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.