Feature Submission De-duplication Engine Using NLP
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Solution Overview
Problem
In complex organizations, duplicative feature submissions for software applications often lead to wasteful utilization of computing resources and non-coordinated work among multiple teams, as multiple stakeholders submit similar ideas without coordination.
Innovation Solution
Implementing a method that uses natural language processing and machine learning to detect and de-duplicate feature submissions, providing notifications, and merging duplicative submissions, while also employing blockchain-based feature management for secure and efficient development management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple stakeholders submit feature submissions independently without coordination, then feature ideation diversity is improved, but duplicative submissions increase and waste computing resources
Solution Approach 1:
The system performs preliminary de-duplication analysis on feature submissions before they are fully processed into the development pipeline. By checking new submissions against existing ones using natural language processing and semantic similarity comparison, the system prevents duplicative work from entering the development workflow, thus avoiding waste of computing resources while maintaining diverse feature ideation.
2Productivity
If multiple teams develop features independently based on similar submissions, then development parallelism is improved, but non-coordinated work increases and reduces efficiency
Solution Approach 1:
The system implements a feedback mechanism where de-duplication results are communicated back to stakeholders and development teams. When a new feature submission is found to be duplicative, the system provides notifications and links to existing feature submissions, enabling teams to coordinate their efforts, merge work, or avoid duplicative development, thus reducing coordination inefficiency while maintaining productive parallelism.
3Quantity of substance
If all feature submissions are processed through manual review, then submission volume is improved, but processing time and resource consumption increase
Solution Approach 1:
The system implements automated self-service de-duplication processing that operates without requiring manual reviewer intervention for each submission. The natural language processing and semantic similarity comparison algorithms automatically analyze new feature submissions, compare them against existing ones, and generate de-duplication results, enabling the system to handle high submission volumes efficiently without proportionally increasing processing time or resource consumption.
Data Source
AI summary
A device may receive a feature submission identifying a candidate feature for a software application. The device may pre-process the feature submission to reformat data associated with the candidate feature to match a data format associated with stored information regarding past feature submissions. The device may perform natural language processing on the data associated with the candidate feature to determine a semantic meaning of the candidate feature. The device may compare the semantic meaning of the candidate feature with an index of past feature submissions. The device may provide a response indicating whether the candidate feature matches the past feature submissions wherein the response comprises an indication that the candidate feature does not match any past feature submission or information identifying one or more past feature submissions associated with a threshold semantic similarity to the candidate feature.