ML Code Prediction via Dialogue Management
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Solution Overview
Problem
Code development is memory-intensive and resource-intensive, often requiring significant time and effort to locate and recall code portions, naming conventions, and usage details, leading to inefficiencies in software development.
Innovation Solution
A system employing machine learning (ML) and natural language processing (NLP) to predict and generate code attributes and portions, using a dialogue management scheme to elicit information and improve code development efficiency by grounding natural language requests in a code space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If developers manually recall and locate code portions, then code development can be performed, but time consumption and resource usage increase significantly
Solution Approach 1:
The system performs preliminary actions by proactively predicting and presenting relevant code portions, attributes, and naming conventions before the developer needs them. The ML model anticipates what code the developer will need next based on contextual analysis of the current codebase and development pattern, thereby eliminating the time spent on manual recall and search.
Solution Approach 2:
The patent introduces an intermediary system comprising a dialogue management component and an ML model that acts as a mediator between the developer and the codebase. This intermediary automatically analyzes code context, retrieves relevant information, and presents predictions to the developer, replacing the direct manual search and recall process with an automated assistance layer.
2Ease of operation
If developers manually manage code attributes and portions, then code development can proceed, but resource intensity and effort increase
Solution Approach 1:
The system implements self-service by enabling the code development environment to automatically manage and predict code attributes, portions, and naming conventions without requiring manual developer intervention. The ML model continuously learns from the codebase and autonomously provides predictions, reducing the cognitive load and manual effort required from developers while optimizing resource usage through intelligent pattern recognition.
3Measurement precision
If comprehensive code analysis is performed to improve prediction accuracy, then prediction quality increases, but system complexity and processing requirements increase
Solution Approach 1:
The patent segments the code analysis task into distinct functional components: a dialogue management component that handles interaction and context gathering, an ML model component that performs prediction, and a learning component that updates the model. This segmentation allows each component to specialize in specific aspects of code analysis, improving overall prediction accuracy while managing system complexity through modular architecture.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to facilitating code development by predicting one or more code attributes and/or code portions for use in a project code to be written. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a dialogue component that generates a query based on a natural language request comprising a code-related attribute, and a prediction component that predicts another attribute or a code portion to satisfy the request. In an embodiment, an input dataset employed to support the influence mapping can comprise time-stamped tuple data comprising a state, an action and a reward. The code-related attribute can at least partially define a project code, of code to be written.