Software Process Prediction Engine for Workflow Change Simulation
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Solution Overview
Problem
Current systems lack an efficient method to determine the impact of changing workflows or resource allocation in software applications without incurring the cost of actual implementation.
Innovation Solution
Employing machine learning models, such as recurrent neural networks (RNNs), to predict software application performance by training a prediction engine with time series data from application execution logs and resource availability, allowing for the estimation of changes in workflows and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If workflow changes or resource allocation modifications are implemented to improve software application performance, then application performance can be optimized, but development costs and implementation time increase due to actual implementation requirements
Solution Approach 1:
The system performs preliminary analysis of workflow changes and resource allocation modifications using machine learning models before actual implementation. By training prediction engines on historical execution logs and resource availability data, the system can forecast performance outcomes of proposed changes in advance, allowing stakeholders to evaluate multiple scenarios without incurring implementation costs or time commitments.
Solution Approach 2:
The system creates virtual copies of the software application's execution environment through simulation. Instead of modifying the actual production system, the prediction engine processes copies of execution logs and resource data to generate predicted performance outcomes. This copying approach enables risk-free evaluation of workflow changes and resource allocation strategies.
2Measurement precision
If comprehensive analysis of workflow changes is performed through actual implementation and testing, then accurate performance impact assessment can be obtained, but development complexity and resource requirements increase
Solution Approach 1:
The system replaces complex mechanical testing and implementation processes with a computational prediction engine based on machine learning. Instead of physically implementing and testing workflow changes in the software system, the invention uses trained neural network models to predict performance outcomes by processing historical execution logs and resource availability data, significantly reducing system complexity while maintaining assessment accuracy.
Solution Approach 2:
The prediction engine acts as an intermediary between workflow design decisions and performance outcomes. Rather than directly implementing and measuring changes in the actual software system, the intermediary prediction engine analyzes execution logs and resource data to forecast performance impacts, simplifying the analysis process while providing accurate assessments.
Data Source
AI summary
An embodiment may involve obtaining a log regarding execution of a software application; obtaining indications of availabilities of resources related to the software application; determining, from the log and the indications of availabilities of the resources, a time series of software application activities; and training a prediction engine with the time series of software application activities, wherein the prediction engine as trained is configured to receive an input time series of further software application activities and generate an output time series that predicts additional software application activities. Another embodiment may involve obtaining an input time series of software application activities, wherein the input time series is based on a log regarding execution of a software application and includes indications of availabilities of resources associated with the software applications; and generating, using a prediction engine, an output time series based on the input time series that predicts additional software application activities.