AI Reverse Engineering Application State Diagrams
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Solution Overview
Problem
Current techniques for understanding and managing applications during transition phases, such as knowledge transfer, face challenges due to unavailable documentation, complex and difficult-to-understand documentation, and the lack of a single source of knowledge, leading to wastage of computing and human resources.
Innovation Solution
An intelligence platform utilizing artificial intelligence and machine learning models to reverse engineer applications from application artifacts by processing user stories, test case documents, event logs, and application logs, generating state diagrams and volumetric analysis, and removing duplicate data to provide insights and conserve resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of application documentation is performed to understand applications during transition, then understanding accuracy may be improved, but time consumption and resource wastage increase significantly
Solution Approach 1:
The patent creates a digital replica or model of the application behavior by processing event logs and application logs to generate state diagrams and volumetric analysis. This copy represents the application's actual runtime behavior, allowing analysts to study the application model instead of manually reviewing extensive documentation, thereby reducing time consumption while maintaining understanding accuracy.
Solution Approach 2:
The system performs preliminary processing of application artifacts (event logs, application logs, user stories, test case documents) to pre-generate state diagrams and volumetric analysis before the actual transition understanding phase. This preliminary action prepares the data in advance, eliminating the need for time-consuming manual review during the transition process.
2Loss of information
If comprehensive documentation is reviewed to understand application behavior, then understanding completeness is improved, but complexity of processing increases
Solution Approach 1:
The patent segments the comprehensive documentation into distinct categories (user stories, test case documents, event logs, application logs) and processes each type separately using appropriate techniques. This segmentation reduces processing complexity by handling different data types independently while still achieving complete understanding through integration of all segments.
Solution Approach 2:
The patent introduces intermediate representations (state diagrams and volumetric analysis) that mediate between the raw comprehensive documentation and the final understanding. These intermediaries simplify the complex documentation into structured visual and quantitative forms, making the information more manageable while preserving completeness.
3Loss of information
If multiple sources of application knowledge are aggregated to ensure complete understanding, then information completeness is improved, but data processing complexity increases
Solution Approach 1:
The patent merges multiple sources of application knowledge (user stories, test case documents, event logs, application logs) into unified outputs (state diagrams and volumetric analysis). The merging process integrates information from all sources while using standardized processing techniques, reducing the complexity that would arise from handling each source separately.
Solution Approach 2:
The patent employs universal processing mechanisms that can handle multiple types of application artifacts through a common framework. The machine learning models and processing pipelines are designed to be multi-functional, accepting different input types (logs, documents, stories) and producing consistent output formats, thereby reducing data processing complexity despite aggregating multiple knowledge sources.
4Productivity
If automated processing of application artifacts is implemented, then productivity is improved, but implementation complexity increases
Solution Approach 1:
The patent implements self-service automation where the system automatically processes application artifacts without requiring manual intervention. The machine learning models autonomously analyze event logs, application logs, user stories, and test case documents to generate state diagrams and volumetric analysis, significantly improving productivity. The automation handles the complexity internally, presenting a simple interface to users.
Solution Approach 2:
The patent replaces manual mechanical processing (human review and analysis of documentation) with automated computational processing using machine learning models. This substitution eliminates the need for manual effort in processing application artifacts, dramatically improving productivity. The implementation complexity is confined to the automated system, which once deployed, operates independently without requiring complex manual procedures.
Data Source
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AI summary
A device may receive input data identifying user stories, test case documents, event logs, and application logs associated with an application, and may perform natural language processing on the user stories and the test case documents, identified in the input data, to generate a first state diagram associated with the application. The device may process the event logs identified in the input data, with a heuristic miner model, to generate a second state diagram associated with the application, and may process the application logs identified in the input data, with a clustering model, to generate a volumetric analysis associated with the application. The device may perform post processing of the first state diagram, the second state diagram, and the volumetric analysis, to remove duplicate data and unmeaningful data and to generate modified outputs, and may perform actions based on the modified outputs.