Machine Learning Code Splicing for Application Testing
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Solution Overview
Problem
Traditional methods for application testing are labor-intensive and inefficient, as they require creating or modifying testing codes for each new or revised application, without adequately addressing indirect implications, necessitating a more automated and effective approach for code analysis and execution.
Innovation Solution
A system utilizing machine learning to identify, splice, and format appropriate testing codes, converting them into compatible formats for execution within a testing environment, while automatically displaying results, leveraging a repository of historical analysis codes and parameters to determine matching codes based on application-specific parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If developers manually create or modify testing codes for each new or revised application, then the testing process can be customized to specific application requirements, but the process becomes labor-intensive and inefficient
Solution Approach 1:
The system copies and reuses historical analysis codes from a repository that stores previously created testing codes. Instead of manually creating new testing codes, the system retrieves existing codes that match the current application's parameters, significantly reducing manual effort while maintaining customization through parameter-based matching
Solution Approach 2:
The system performs automated code splicing and formatting operations without requiring manual developer intervention. The machine learning model automatically identifies relevant historical codes, splices them together, and formats the output, allowing the system to serve itself in the code generation process
2Loss of time
If developers recycle and modify existing application testing codes, then some work can be reused, but the process remains labor-intensive and does not address indirect implications of the application being tested
Solution Approach 1:
The system replaces manual mechanical processes of code selection, splicing, and formatting with automated machine learning operations. The ML model automatically performs code retrieval, concatenation, and formatting based on application parameters, eliminating the need for manual developer intervention in these tasks
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing historical analysis codes in a structured repository with associated parameters. This preliminary organization enables rapid automated retrieval and matching, eliminating the need for manual code search and adaptation during the testing preparation phase
3Adaptability or versatility
If traditional testing methods are used without machine learning, then the process is simpler to implement, but it lacks the ability to automatically identify and match appropriate testing codes based on application parameters
Solution Approach 1:
The machine learning model serves multiple functions within the system: it retrieves historical codes from the repository, matches them to current application parameters, splices selected codes together, and formats the output. This multi-functional approach enables adaptive code generation without requiring separate systems for each operation
Data Source
AI summary
Embodiments of the present invention provide a system for identifying, splicing, and formatting code via machine learning for application analysis. In particular, the system may generate a repository that links multiple historical codes with associated analysis parameters. When a request for a new set of analysis codes is requested for analyzing a first application, the system can determine a set of analysis parameters associated with that first application and determine the set of analysis codes for analyzing the first application through machine learning analysis of the repository. The system may then generate the set of analysis codes by splicing together the determined set of analysis codes and formatting the codes into a format that is compatible with the first application. The system may then automatically execute the code on the first application within a testing environment and display the results on a computing device of an associated user.