Automated Model Generation for Software Systems
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Solution Overview
Problem
The testing and development of complex multi-tiered or distributed software systems are hindered by the complexity and time-consuming process of constructing testing environments, which often requires hundreds of man-hours and involves human-performed workflows, leading to bottlenecks in generating and launching models for software component simulations.
Innovation Solution
A system that automatically generates and launches models from transaction data collected through monitoring, using agents to instrument software components, transaction path engines to identify transaction fragments, and filter managers to select relevant data for model generation, allowing for autonomous creation and deployment of virtual services and test cases without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated model generation is implemented, then productivity and development speed improve, but system complexity increases
Solution Approach 1:
The system segments the model generation process into distinct functional modules: transaction data collection, transaction path analysis, model generation, and model launching. Each module handles a specific aspect of the process, making the overall complex system manageable and maintainable while achieving high automation.
Solution Approach 2:
The patent introduces intermediary components such as the transaction path engine and filter manager that mediate between raw transaction data and the final model output. These intermediaries process and structure data appropriately, enabling automated model generation without requiring direct complex interactions between all system components.
2Loss of time
If manual workflow is used for constructing testing environments, then model generation accuracy can be controlled, but time consumption increases significantly
Solution Approach 1:
The system enables self-service automation where the model generation process operates autonomously based on collected transaction data. The automated generation of models from transaction data eliminates manual intervention while maintaining accuracy through systematic data-driven approaches, significantly reducing time consumption.
Solution Approach 2:
The system incorporates feedback mechanisms where transaction data collected from actual system operations is used to generate and refine models. This feedback loop ensures that automated model generation produces accurate representations of system behavior while operating autonomously, resolving the contradiction between automation level and time consumption.
3Measurement precision
If comprehensive transaction data is collected for model generation, then model accuracy improves, but data processing complexity increases
Solution Approach 1:
The filter manager extracts only the relevant portions of transaction data needed for model generation, separating essential information from unnecessary data. This extraction process maintains model accuracy by focusing on critical transaction attributes while reducing data processing complexity by eliminating redundant information.
Solution Approach 2:
The transaction data is segmented into distinct components and attributes that are relevant for different aspects of model generation. This segmentation allows the system to process comprehensive data for accuracy while managing complexity by handling data in organized, manageable segments rather than as a monolithic whole.
Data Source
AI summary
Transaction data is accessed that has been generated from monitoring of a plurality of transactions in a system that includes a plurality of software components. The transaction data is assessed to identify that a particular one of the plurality of transactions meets a particular one of a set of conditions based on an attribute of the particular transaction. A portion of the transaction data describing the particular transaction is selected based on the particular transaction meeting the particular condition. A model of a particular one of the software components involved in the particular transaction is generated using the selected portion of the transaction data. The model is used to launch a computer-implemented simulation of the particular software component within subsequent transactions of the system.