Machine Learning Logic Flow Automation
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Solution Overview
Problem
Software developers face inefficiencies in defining and executing server-side logic, as they must manually specify and validate each step in a process flow, making it cumbersome and time-consuming, especially when using the same development tool for multiple mini applications.
Innovation Solution
A computer-implemented method using a machine learning engine to suggest subsequent tasks based on previous user frequencies, allowing developers to select and add stages to a logic flow with drag-and-drop interaction, reducing the need for manual validation and streamlining the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If developers manually specify and validate each step in a process flow, then the logic flow is accurate and complete, but the process is cumbersome and time-consuming
Solution Approach 1:
The system performs preliminary action by automatically generating suggested subsequent tasks and validating logic flow steps before the developer completes the manual specification. The machine learning engine pre-computes recommended tasks based on historical data, and the system pre-validates the logic flow structure, reducing the time developers need to spend on manual specification while maintaining accuracy through guided suggestions.
2Adaptability or versatility
If developers use the same development tool for multiple mini applications, then tool consistency is maintained, but the development process becomes repetitive and inefficient
Solution Approach 1:
The system implements feedback by analyzing historical development data from multiple mini applications and using machine learning to generate suggestions for subsequent tasks. This feedback loop allows the system to learn from past development patterns across different applications while maintaining tool consistency, thereby improving productivity by reducing repetitive manual work without sacrificing the benefits of using the same development tool.
3Reliability
If developers click on every stage to validate properties and mappings, then the logic flow is thoroughly validated, but the process is tedious and reduces productivity
Solution Approach 1:
The system applies self-service by automatically validating logic flow stages, properties, and mappings without requiring developers to manually click through each stage. The machine learning engine and validation system autonomously check for errors, inconsistencies, and completeness, maintaining thorough validation while dramatically improving development speed by eliminating the tedious manual verification process.
Data Source
AI summary
A method for designing a logic flow for a user interface is provided. The method comprises receiving from a first task for an automation process file from a user. The automation process file defines a set of system activities. The first task is added to the automation process file and submitted to a machine learning engine, which determines a number of suggested tasks to be performed after the first task. The suggested tasks are based on frequencies with which previous users have used each task after the first task. The suggested tasks are then presented to the user. A second task is received from the user to be performed after the first task. The second task may be selected from the suggested tasks but not necessarily so. The second task is then added to the automation process file.